Conference Agenda

High-rise buildings are fundamental components of modern urban infrastructure, and their structural safety under dynamic loads such as wind and earthquakes is critically important In recent years, ground-based radar interferometry has been widely employed for monitoring vibrations and deformations in tall structures, owing to its high precision, non-contact operation, and full-field measurement capability. In practical monitoring, displacement signals are affected by various types of noise, leading to unstable and nonlinear variations in the signal. This makes it difficult to accurately obtain structural vibration characteristics (such as frequency and damping ratio) and micro-deformation data with precision. Conventional denoising techniques are often applied for noise reduction. Nevertheless, these methods exhibit notable limitations. Bandpass filtering requires a predefined frequency passband, becoming ineffective in cases of spectral overlap between signal and noise. Wavelet denoising lacks adaptability due to its strong dependence on the selected wavelet basis and decomposition level, often introducing signal distortion. Kalman filtering, meanwhile, relies on an accurate state-space model, the construction of which is challenging for complex high-rise structures, thereby limiting its practical utility. In response to these challenges, this paper proposes a fully adaptive denoising method based on CEEMDAN, incorporating dual criteria of energy distribution and correlation. The proposed approach effectively processes non-stationary and nonlinear signals while avoiding the subjectivity associated with basis selection in traditional methods. It significantly improves both mode separation accuracy and denoising reliability, establishing a robust foundation for structural state assessment and safety early warning based on radar monitoring data.

Localisation remains a crucial aspect of robotic design. It forms the basis of any kind of autonomous navigation for drones, cars and other specialized robots. This is usually achieved using a Simultaneous Localisation and Mapping (SLAM) algorithm, which uses an input sensor to localise the robot within a map that is created simultaneously. The input sensors are either cameras, which provide visual data, or Light Detection And Ranging (LiDAR) sensors, which automatically deliver a point cloud up to surveying quality. In recent years, LiDAR inertial odometry (LIO) algorithms, which combine measurements from a LiDAR sensor and inertial measurements from an IMU, have become more popular. These algorithms do not use a previously recorded map, but rather create their own map during runtime. This paper contributes an improvement to the precision by integrating a predefined 3D global point cloud map into the localisation algorithm. Over the course of multiple experiments in different testing scenarios, we have achieved a 71% reduction of the distance error for localisation, while there was no significant change regarding the orientation error. This makes the presented system a suitable localisation option for real-world robotic operations at construction sites.

Reliable visual localization is essential for long-range VTOL UAV mapping in GNSS-degraded environments. This paper presents a quantitative evaluation framework for monocular ORB-SLAM3 using a 66.48 km multi-altitude UAV mission and aerial-triangulation-derived camera poses as reference data. The workflow associates SLAM and reference trajectories by image key, applies Sim(3)-based metric alignment, corrects coordinate-axis inconsistency, and refines attitude by a global rotation offset, enabling full-mission and segment-level comparison in a common metric frame. The evaluation covers four altitude segments, namely 100, 150, 200, and 250 m AGL, under three protocols: No-Loop (NL), With-Loop Global Slice (GS), and With-Loop Local Re-Sim(3) (LR). For the full mission, the proposed alignment achieves a 3D position RMSE of 7.41 m over 5330 matched frames and substantially reduces the geometric deformation observed in the S+T baseline. Segment-level results show a strong altitude dependency in the isolated NL runs, with 3D RMSE decreasing from 22.95 m at 100 m to 5.49 m at 250 m. Among the three protocols, LR consistently yields the best segment-level position accuracy, reaching 4.00, 8.26, 3.94, and 3.92 m at 100, 150, 200, and 250 m, respectively. Long-range analysis further shows that the trajectory remains globally bounded, while cumulative 3D endpoint drift increases from 0.35 m at 50 m to 10.66 m at 25.6 km. These results indicate that ORB-SLAM3 can support large-scale trajectory estimation for UAV mapping, but its evaluated quality depends strongly on alignment, segmentation, and evaluation strategy.

Tropospheric delays significantly hinder accurate InSAR deformation mapping, and their complex spatiotemporal vari-ability makes effective mitigation challenging. When GNSS are available, conventional functional models interpolate GNSS-derived delays to unobserved locations, but their low-order form mitigates only long-wavelength errors and neglects the stratified component. In phase-based correction, temporal low-pass filters such as the Gaussian filter suppress high-frequency turbulence but ignore the strong distance/elevation-dependence of tropospheric delays, making the results highly sensitive to the chosen time window .

In response, we adopt a Least Squares Collocation (LSC) scheme, an effective approach that treats spatially correlated turbulence as a stochastic variable, characterizes it through a variance–covariance model , and estimates it jointly with other deterministic parameters. With external GNSS data, a joint correction that accounts for both the strati-fied and turbulent components is constructed, and are simul-taneously estimated using LSC. For the data-driven case, the deformation phases are parameter-ized by a time-domain polynomial, while the turbulence are treated as spatially correlated stochastic variables defined by spatial variance-covariance functions. LSC is employed to estimate the deformation model parameters through sliding time-window filtering process .

We design a voice-interactive indoor positioning method that jointly utilizes spatial “near” relationships extracted from verbal descriptions and multiple sensor sources

Real-time dense SLAM must balance geometric fidelity and computational efficiency for autonomous navigation. Explicit mapping methods provide stable global structure and fast updates, but suffer from discretization artifacts and memory overhead. Implicit neural representations capture continuous surfaces and fine details, yet require expensive optimization and are sensitive to initialization. Existing hybrid approaches combine both paradigms, but often allocate neural refinement inefficiently and remain vulnerable to pose errors. To address these limitations, we propose a selective hybrid dense mapping framework that couples a scene-wide TSDF backbone with quality-guided implicit local refinement and residual-guided sliding-window pose feedback. Neural refinement is activated only in low-quality regions identified by multi-indicator assessment, while keyframe poses are re-optimized using residuals from explicit raycasting and implicit rendering. Experiments on TUM RGB-D and Replica demonstrate improved mapping accuracy, localization robustness, and real-time efficiency.

We present a simultaneous calibration of the 6 installation parameters (3 boresight angles and 3 lever arm offsets) for a mobile LiDAR system on a hydrographic platform using spherical targets. This algorithm finds the boresight angles and lever arm offsets that minimize the sum of positive distances from points to their corresponding sphere surfaces. The calibration method is first developed and tested using synthetic data generated by a ray-tracing algorithm using a line-sphere intersection model and is subsequently validated using real scan data. The spherical targets are installed on tripods at the calibration site, where their centers are surveyed using postprocessed GNSS observations. The RMS error for the distance between the surveyed sphere centers and the fitted sphere centers is 3.6 cm, which we attribute to the propagation of GNSS and LiDAR scanner errors. The MATLAB code developed for the simultaneous estimation of a complete set of 6 LiDAR installation parameters using spherical targets is available as open-source software on GitHub.

Efficient road surface condition evaluation is critical for ensuring transportation safety, maintaining ride quality, and supporting informed pavement management decisions. Conventional methods such as laser profilers and specialized inertial systems provide highly accurate measurements but are expensive, limited to specialized vehicles, and difficult to scale for network-level monitoring. To address these limitations, this study presents an accelerometer-based framework that leverages survey-, mapping-, and consumer-grade GNSS/INS units to detect pavement surface anomalies in a cost-effective and scalable manner.

Vertical acceleration data were collected using three inertial systems mounted on the Purdue Wheel-based Mobile Mapping Systems: the high-accuracy PWMMS-HA, the ultra-high-accuracy PWMMS-UHA, and a compact low-cost OpenIMU paired with a SparkFun GPS-RTK2 unit. All systems were driven along a 59 km closed-loop roadway network in West Lafayette, Indiana, capturing diverse pavement conditions under identical driving trajectories. The proposed pipeline includes two complementary anomaly detection approaches. The first applies an Isolation Forest model, an unsupervised machine learning technique that identifies abnormal vibration patterns using statistical window-based features. The second employs an Adaptive Threshold method that flags acceleration windows exceeding a dynamic statistical threshold. Both methods categorize detected anomalies into mild, moderate, and severe levels.

Across all IMU grades, the Isolation Forest detected 962–965 anomalies, while the Adaptive Threshold identified 989–996 anomalies, with more than 91% spatial agreement between sensors and over 96% consistency between detection approaches. Results demonstrate that even low-cost inertial sensors reliably capture pavement disturbances.

Accurate three-dimensional (3D) data acquisition of indoor environments remains a challenging and resource-intensive task, particularly for fully furnished spaces. This study presents the development and implementation of a low-cost wearable surveying system for efficient 3D indoor data acquisition. The proposed system integrates a Livox Mid-360 LiDAR sensor and an RGB camera mounted on a helmet, both controlled via a min-PC unit for synchronized data collection. The captured LiDAR frames and inertial measurement unit (IMU) data are fused with RGB imagery using a Simultaneous Localization and Mapping (SLAM) framework to generate 3D reconstruction of interior structures. The resulting point clouds and wall models are evaluated based on RANSAC line fitting method to assess their geometric accuracy and structural consistency. Moreover, a ground truth measurements were collected to verify the absolute accuracy of the resulting point clouds. The proposed approach demonstrates the potential of cost-effective, portable solutions for indoor 3D mapping and documentation with a cm-level of accuracy.

Hongtu-1 (HT-1) InSAR satellite, as an innovative multi-baseline X-band InSAR system, employs a four-satellite cartwheel formation to achieve single-pass interferometry. HT-1 has been extensively used for topographic mapping, disaster monitoring, and assessment. This research focuses on assessment of HT-1's capability for high-precision forest vertical structure inversion, especially for forest height estimation (X-band SAR is sensitive to canopy of forest due to the higher frequency). This paper develops a complete interferometric processing for HT-1 multi-baseline data. By applying two representative InSAR techniques to HT-1 multi-baseline InSAR data, this results demonstrate the forest vertical structure profile and canopy height map derived over the test site, which shows good agreement with the LiDAR data. The results confirm HT-1's feasibility for tomography and demonstrate the potential of multi-baseline satellites for future missions.

Hyperspectral images (HSIs) provide rich spatial–spectral information for applications such as environmental monitoring, land cover mapping, and mineral exploration. However, their practical utility is often severely degraded by mixed noise (Gaussian, impulse, and other unstructured components), striping artifacts, and partially missing data, especially in bands affected by strong water vapor absorption. Existing methods typically treat these degradations separately and struggle to jointly correct them within a unified framework.

This work presents an integrated HSI reconstruction method that couples a low-rank decomposition model with a hybrid supervised–unsupervised deep architecture. The HSI is factorized into spatial abundance maps and spectral endmember signatures, which are respectively modeled by a Transformer-based abundance reconstruction network and a 1D convolutional endmember smoothing network. The abundance network is first supervisedly pre-trained on large-scale natural image datasets and then fine-tuned, together with the spectral network, using unsupervised loss terms tailored to spatial and spectral fidelity. A weighted group-sparse regularization is further introduced to explicitly capture striping noise and constrain the learned subspaces.

Extensive experiments on simulated Washington DC Mall data and real Gaofen-5 (GF-5) satellite imagery demonstrate that the proposed method effectively suppresses unstructured noise, removes striping artifacts, and recovers missing information, achieving superior visual quality, higher spectral fidelity, and fewer artifacts compared with state-of-the-art baselines.

Static Terrestrial Laser Scanning (TLS) and SLAM-based Portable Mobile Laser Scanning (PMLS) are increasingly adopted in Cultural Heritage (CH) documentation, but their suitability for Historic/Heritage Building Information Modelling (HBIM) depends on both data quality and acquisition conditions. This paper compares a 2022 TLS survey and a 2025 handheld PMLS survey of San Giacomo Church (Como, Italy) to assess whether the latter can reliably support HBIM-oriented documentation. The methodology combines dataset-level comparison and ROI-based analysis on four stable architectural elements: apsis, pillar, timber roof truss, and central dome. Three complementary metrics were used: a local density proxy, a scale-dependent coverage ratio, and M3C2 distance statistics for geometric agreement. Results show that PMLS is consistently less dense than TLS but remains effective for 1:100 scale documentation and, in several cases, also for 1:50. Statistics on M3C2 distance remain generally within centimetric ranges, indicating good local agreement where surfaces are effectively observed. The study demonstrates that sensor suitability not only depends on the geometric complexity but also on sensor-to-surface distance, visibility, and acquisition geometry, supporting hybrid TLS–PMLS workflows for CH HBIM.

This study examines the potential of RTK GNSS-integrated close-range photogrammetry for documenting cultural heritage without the need for ground control points (GCPs). The research focuses on evaluating the GEOSTIX-X5 GNSS receiver, which enables direct synchronisation with a camera via the flash hot shoe, providing precise time-stamping of image capture events. The case study was conducted at Tomb 8 of the Tombs of the Kings in Paphos, a UNESCO World Heritage Site, and compares two datasets: a conventional photogrammetric survey from 2022 using GCPs and a 2025 survey employing GNSS-assisted photogrammetry. Both terrestrial and UAV imagery were acquired and processed in Agisoft Metashape, with accuracy assessment performed through cloud-to-cloud comparison in CloudCompare. Results indicate that the GNSS-integrated approach achieved single centimetre-level accuracy and no systematic scale errors. The findings demonstrate that RTK GNSS-assisted photogrammetry can significantly reduce fieldwork complexity while maintaining high accuracy, offering a promising alternative for heritage documentation where GCP placement is impractical or undesirable.

UAS photogrammetry has become an efficient solution for acquiring high-resolution geospatial data for urban mapping, environmental monitoring, and 3D modelling. However, mission planning still involves a trade-off between data quality and operational efficiency, particularly regarding flight altitude, which directly affects ground sample distance (GSD), point cloud density, and positional accuracy. This study evaluates the influence of flight altitude through a controlled comparison of two urban photogrammetric surveys: a low-altitude flight at 61.2 m (GSD = 1.56 cm/pix, 420 images) and a higher-altitude flight at 121 m (GSD = 3.11 cm/pix, 116 images). Both surveys used RGB cameras with equivalent image resolution mounted on different platforms, which constitutes an experimental limitation, while overlap and processing parameters were kept constant. The results show that the lower-altitude flight produced denser data and better geometric performance, with lower reprojection error and lower check point RMSE. In contrast, the higher-altitude flight provided greater operational efficiency, covering a larger area with fewer images and lower computational demand. These findings indicate that both strategies are technically viable but suited to different objectives: lower altitudes favour geometric detail and positional accuracy, whereas higher altitudes improve productivity and area coverage. Therefore, flight altitude should be selected according to project requirements, balancing geometric quality and operational efficiency.

Advances in optical measurement technologies have increased demands for accuracy, speed, and automation in coordinate metrology. This contribution introduces CENAGIS-MET, a metrological verification standard developed at the Warsaw University of Technology for assessing active range-based systems such as terrestrial laser scanners (TLS). Unlike traditional calibration fields designed for small ranges, CENAGIS-MET enables evaluation over large measurement areas using modified VDI/VDE guidelines.

The methodology incorporates probing error, sphere-spacing error, and flatness assessment using high-precision ceramic artefacts. Tests were conducted on Leica RTC360, Leica Nova MS60, Z+F 5006h, and a handheld Livox-based scanner (Mendeye). Results show RTC360 and MS60 fully meet the 1/5 relative error criterion, confirming suitability for engineering-grade applications. Z+F 5006h achieves partial compliance, requiring careful configuration, while Mendeye exceeds permissible thresholds, limiting its use to qualitative documentation.

In the full version of the article, broader analyses will be provided regarding the accuracy of 3D shape reconstruction used for the probing error, as well as roughness and planarity assessment for evaluating the overall distribution of the reference plane

Accurate topo-bathymetric shoreline mapping and semantic segmentation of remote sensing imagery are fundamental to monitoring dynamic coastal systems, with significant implications for sustainable management, ecological preservation, and climate resilience planning. In vulnerable regions such as Lake Huron, Ontario — where sensitive ecosystems face growing anthropogenic pressures—precise delineation of land-water interfaces enables critical applications including coastal habitat mapping, sediment flux quantification, and erosion vulnerability assessment. This study presents a training-free, open-vocabulary segmentation framework that adapts frozen vision-language models (VLMs) to automatically extract shoreline features from near-infrared (NIR) imagery. By harnessing the inherent semantic reasoning abilities of VLMs, the method achieves accurate segmentation without relying on large, annotated datasets. Extensive evaluation on the Fathom Topo Bathymetric Dataset demonstrates the model's robustness across diverse nearshore environments, highlighting its applicability as a scalable solution for coastal mapping. This research underscores the potential of integrating foundational vision language models into geospatial workflows to enable automated, high-resolution environmental monitoring in data-limited settings.

The coastal zones consist of the interfaces between land and sea, undergoing the mobility of the shoreline at an unprecedented pace over the last centuries. Such a trajectory, at the global scale, exacerbates the coastal risks (intersecting hazards, exposure and vulnerability), calling upon a scalable methodology to ensure the precise and accurate monitoring. One of the observation solutions resides in the satellite platform provided with the finest spatial and spectral resolutions. Because remote sensing is a science of trade-offs, no sensors can be both excellent in spatial and spectral specificities.

We propose an original research study to create an imagery endowed with both high spatial and spectral characteristics, purposed to classify a representative coastal zone (12 habitat classes) in a temperate area in Brittany, France. The methodology highlights a transferable fusion procedure based on the simultaneous acquisition (10-min difference) of the 30-m hyperspectral PRISMA satellite imagery and the 3-m PlanetScope (Dove-R) imagery, made possible given the very high temporal resolution of the PlanetScope constellation. The spatial resolution of the hyperspectral PRISMA imagery, in the visible and near-infrared spectrum (63 bands), was successfully upscaled at 3 m, using a bandwise linear prediction from the 4 PlanetScope Dove-R bands (collected at 3 m). The model residuals showed that the pansharpened PRISMA imagery (5 m) was better enhanced (absolute deviation of 0,011) than the original PRISMA imagery (30 m, absolute deviation of 0,015). Seawater and mudflat were the best habitats upscaled, whereas the road and the roof were the worst classes predicted.

The evolution of mapping platforms has followed a consistent pattern: professional instruments are complemented by consumer devices that trade precision for scalability. Unmanned aerial systems transformed aerial photogrammetry by making it accessible beyond traditional aircraft, and smartphones equipped with RTK have demonstrated viable terrestrial mapping. This paper extends that progression to vehicle-based mapping by presenting SurveyXR, a web-based calibration and georeferencing framework that converts consumer vehicle dashcam video into photogrammetry-ready georeferenced imagery. The system addresses two technical problems: determining the geometric relationship between uncalibrated consumer cameras and a known navigation trajectory and producing per-frame exterior orientation parameters suitable for Structure-from-Motion processing. This pipeline implements checkerboard-based intrinsic calibration with automated quality diagnostics, Perspective-n-Point exterior orientation solving, and GNSS-synchronized frame extraction with lever arm correction. All computation runs in a browser or lightweight cloud backend, requiring no local software installation. The framework was tested on a 2026 Tesla Model Y equipped with PPK GNSS on the Ohio State University campus. Georeferenced frames were verified against the GNSS trajectory, confirming correct spatial positioning. The paper documents the calibration methodology, time synchronization model, and coordinate geometry, and discusses error sources and the path toward quantitative accuracy validation.

In this paper, we describe a modern, efficient, accurate and reliable stitching algorithm that JOANNEUM RESEARCH (JR) has developed for the PhaseOne PAS Pana multi-camera system. We present a new "constraint" projective transformation (CPT) approach, reducing the eight parameters of a standard projective transformation to only six, physically meanigfull parameters: Correction scale, parallax in x- and y-direction and three relative orientation angles. Based on the CPT, tie point measurements of all available image overlaps (NIR/NIR, RGB/NIR and RGB/RGB) are adjusted simultaneously within a common virtual image plane. As the CPT contains no over-parametrization any more for modelling the relative orientation of the (calibrated) camera modules we expect a more accurate and stable stitching result which will be evaluated by analysing the stitching parameters of consecutive PAS Pana shots of a flight line.

This paper presents tests of a new fully integrated multi-sensor airborne system that comprises LiDAR, multiple cameras, inertial measuring unit (IMU), GNSS, and their associated software for data acquisition, processing, integration, calibration, and map production. The technical analysis presented in this paper focuses on multi-sensor system integration that statistically addresses a multi-stream of LiDAR ranges, pixels from multiple cameras, position and orientation of each LiDAR range and each photo center derived from the GNSS/IMU trajectory. The impact of processing the trajectory in two different ways, namely: Post-Processed Kinematic (PPK using Single Base Station) and Trimble Post-Processed RTX (PP-RTX) is evaluated. Real-world data sets acquired with the Vexcel UltraCam Dragon in Austria and USA are used in this paper to address system performance in a real-world environment. Test results confirm the suitability of both approaches for trajectory processing, Single Base and PP-RTX, as well as the consistent positional accuracy of georeferencing solutions for imagery and lidar.

Matrix detectors and colour filters arrays are more widely used for satellites and rover missions in the past years. Recently, four CO3D (from “Constellation Optique 3D” in french) satellites equipped with COTS matrix Bayer sensor were launched and calibrated. Both the sensor sampling distinctive features and the new Step & Stare guidance mode are leading to new calibration and processing paradigms. In this paper, we delve into techniques dedicated for such Bayer matrix-based system, mainly but not limited to high-resolution (HR) Earth-observation (EO) satellite missions. We first describe dedicated techniques for in-orbit radiometric performance assessment like signal-to-noise ratio (SNR) and modulation transfer function (MTF). Then we address ground processing dedicated to Bayer acquisitions. Finally, we demonstrate the validity of our approach with CO3D in-orbit measurements. We also apply the radiometric ground processing on real images and provide a comparison with Pléiades-HR imagery, demonstrating the many benefits of the CO3D mission and all its novelties. CO3D in-orbit testing (IoT) is still ongoing eight months after launch, the in-flight performances are not presented in this paper due to confidentiality agreement.

This study presents a systematic evaluation of the relative geometric accuracy of UAV photogrammetry over drifting Arctic sea ice, addressing critical challenges posed by textureless surfaces and dynamic motion. Utilizing data from 18 shipborne UAV flights during the FACE2024 expedition, the research quantifies the impact of sea ice drift on orthomosaic horizontal accuracy. A methodological framework is established that incorporates shipborne GNSS data for drift correction, aligning image positions to a common reference frame under the assumption of consistent icebreaker–ice motion. Accuracy assessment is performed using onboard control and check lines of known lengths, enabling reliable relative error measurement without traditional ground control points (GCPs), which are infeasible on drifting ice.

Results demonstrate that drift velocity and total drift distance have a strong positive correlation with root mean square error (RMSE) before correction (r = 0.70 and r = 0.79, respectively), while the flight-drift angle has minimal influence (r = –0.13). The application of ship-position-based drift correction significantly improves accuracy, reducing RMSE by an average of 0.23 m and achieving a high relative accuracy of approximately 10 cm for imagery with 2–4 cm ground sampling distance. The use of control lines alone also substantially enhances results. This work validates the efficacy of drift correction and provides practical guidance for mission planning and data processing, confirming that standard UAVs and commercial photogrammetric software can produce reliable results in challenging polar environments when appropriate corrections are applied.

This paper examines how to align PlanetScope and Sentinel-2 vegetation indices, focusing on the Normalized Difference Red Edge (NDRE) index, which is commonly used in precision agriculture for prescription maps. While Sentinel-2 is popular for crop monitoring, its low spatial resolution limits use in small or irregular fields. PlanetScope provides higher-resolution, more frequent imagery, but its sensor differs from the Sentinel-2, limiting compatibility with current research and tools. By testing three adjustment methods, the study shows that it is possible to align PlanetScope NDRE values with Sentinel-2: M1 (Linear Regression + Histogram Shifting + Histogram Matching), M2 (Histogram Matching), and M3 (per-band linear regression before index calculation). Two dates from 2022 were selected as representative seasonal extremes from the broader 2021–2023 dataset of 56 image pairs (Baldin, 2025), which was further analyzed through time-series methods. Resampling direction (PS→10 m, S2→3 m) minimally affects RMSE/MAE but significantly alters spatial structure and Moran’s I values; downscaling PS to 10 m decreases Moran’s I. M2 is suitable for standard applications, whereas M3 is preferable when preservation of spatial structure is important. Across the four examined scenarios, all methods reduce RMSE below the 0.07 agronomic threshold, with calibrated RMSE ranging from 0.02 to 0.05 (up to 0.06 across the full 56-pair dataset). M3’s advantage lies in how effectively it reduces spatial autocorrelation mismatch: a 43.4% reduction in Moran’s I (versus ~18.2% with M1 and M2) in the four example scenarios, and 39.5% versus 28.4% (M1) and 28.2% (M2) reduction over the full dataset.

This study introduces an integrated airborne sensor system that combines mid-wave infrared (MWIR) imaging, a high-resolution aerial camera, and GNSS/IMU navigation for all-day, high-precision remote sensing. The MWIR subsystem adopts a Frame-scanning mechanism to achieve wide-swath and efficient thermal data acquisition. A unified calibration and synchronization framework was developed to ensure temporal and spatial consistency among sensors, including precise time synchronization, lever-arm and boresight calibration, and radiometric correction. The refined GNSS/IMU trajectory supports accurate co-registration between MWIR and optical imagery. Field experiments in China demonstrated stable system performance and consistent geometric–radiometric alignment under various illumination conditions. The integrated dataset enables detailed thermal–optical reconstruction, revealing thermal features and material contrasts not observable in visible imagery. The system supports applications such as infrastructure inspection, environmental monitoring, and emergency response. With its compact structure and modular design, the proposed platform provides a practical reference for next-generation airborne sensor integration and real-time data fusion in high-resolution mapping missions.

Existing satellite mission-planning algorithms are primarily designed for homogeneous single-payload constellations, making them insufficient for coordinating heterogeneous satellites such as optical and SAR systems. Moreover, most current approaches rely on highly abstract task models that neglect the fact that a single satellite may operate under multiple observation modes, each imposing distinct constraints on geometry, attitude maneuvering, and resource utilization. In addition, few studies have addressed the integrated scheduling of point-target and area-target missions, which is essential for scenarios combining discrete and continuous observation demands.

This study proposes an integrated scheduling algorithm for multi-mode, multi-scenario, and multi-task Earth-observation constellations. The algorithm formulates mission planning as a unified spatiotemporal optimization problem, jointly considering visibility, sensor compatibility, attitude feasibility, and onboard resource limits. A CDCL-enhanced constraint-programming solver is employed to enable coordinated scheduling across different observation modes and target types.

Experimental validation on hydropower and disaster-monitoring scenarios shows that the proposed method significantly improves coverage, cross-sensor synergy, and responsiveness compared with traditional homogeneous schedulers. The results establish a new paradigm for integrated and intelligent mission planning of heterogeneous, multi-mode satellite constellations.

Navigating Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments requires reliable autonomous localization techniques. This study proposes a vision-based localization framework utilizing satellite true orthophotos and Digital Surface Models (DSMs) as absolute geospatial references. The algorithmic pipeline integrates deep learning architectures—specifically SuperPoint and LightGlue—to establish robust image-to-map feature correspondences. The matched correspondences are used to estimate camera exterior orientation parameters through collinearity-based spatial resection with an Iteratively Reweighted Least Squares (IRLS) approach. To validate the proposed methodology, a multi-altitude dataset (100–250 m) was acquired across structurally diverse terrains, including dense building, high vegetation, and bare ground areas. Experimental evaluations demonstrate that the framework achieves meter-level absolute positioning accuracy and stable pose estimation. Analyses further reveal that matching robustness and localization success rates depend heavily on terrain texture and flight altitude; geometrically structured urban scenes at moderate-to-high altitudes consistently yield reliable correspondences, whereas low-texture environments and lower flight altitudes present persistent challenges for continuous visual tracking.

Recent handheld scanners increasingly integrate geometric (LiDAR-based) and visual (image-based) SLAM (Simultaneous Localization And Mapping), promising low-cost and flexible solutions for surveying tasks. This paper evaluates the accuracy of three such systems: the XGRIDS Lixel K1, the SHARE S20, and a Pix4D solution pairing an iPhone Pro with an Emlid Reach RX GNSS (Global Navigation Satellite System) antenna. We conducted experiments in two distinct environments: Scene 1, with continuous, high-quality RTK (Real-Time Kinematic) coverage, and Scene 2, which included an indoor trajectory resulting in a temporary loss of the RTK fix. Accuracy was validated against independent GNSS check points. In Scene 1, the Pix4D solution delivered survey-grade results, achieving a RMSE (Root Mean Square Error) below $3~\text{cm}$ in the $X, Y$, and $Z$ directions. The XGRIDS and SHARE scanners yielded larger maximum errors, around $10~\text{to }15~\text{cm}$. In Scene 2, accuracy degraded; the Pix4D solution's maximum error increased to approximately $12~\text{cm}$ , while the Share S20's maximum error exceeded $25~\text{cm}$. We conclude that while the fusion of visual and geometric SLAM is powerful, a stable RTK fix remains critical for achieving consistent survey-grade accuracy with current low-cost handheld scanners

Stem diameter is a key parameter for assessing woody vegetation growth and its ecological and economic benefits, including biomass production, carbon sequestration, and urban ecosystem services. Recent advances in handheld mobile laser scanning (HMLS) enable efficient acquisition of high-density point clouds for deriving tree structural attributes in complex environments. This study presents an automated workflow for tree detection and diameter at breast height (DBH) estimation in an urban park, using two HMLS systems: the GoSLAM RS100i and the FJD Trion S1. The influence of point cloud density and subsampling resolution (2 - 4 cm) on detection and accuracy was evaluated. Reference data for 69 trees were collected using a forestry caliper and total station, while HMLS datasets were georeferenced with RTK-GNSS. The workflow included point cloud filtering, terrain modelling, stem extraction, and DBH estimation through cylindrical fitting. Detection performance differed between systems and was strongly affected by point density. The GoSLAM RS100i detection rate decreased from 97.1% at 2 cm to 53.6% at 4 cm spacing, whereas the FJD Trion S1 maintained stable performance (~87%) across all resolutions, likely due to higher point density. DBH estimation accuracy was similar for both systems, with RMSE values of 3.3–3.6 cm for filtered data and up to 4.9 cm when all detections were included, alongside a consistent positive bias (1.7–2.5 cm). Subsampling had no significant effect on DBH accuracy, indicating robustness to moderate density reductions. Overall, HMLS systems provide reliable DBH estimates in urban environments, with performance mainly influenced by point cloud quality.

In general, the obstacle detection systems mainly rely on depth cameras or AI-based vision approaches; however, these methods are often constrained by limited fields of view and the need for continuous model retraining to adapt to complex and dynamic industrial scenes. To overcome these limitations, this study proposes a LiDAR-based obstacle detection and field monitoring system integrated with a quadruped robot. The proposed system focuses on three main components: real-time field mapping, intelligent path planning with obstacle avoidance, and field change detection. LiDAR point cloud data are pre-processed using pass-through and voxel grid filters, followed by coordinate transformation into the robot reference frame. The Cartographer simultaneous localization and mapping (SLAM) algorithm are employed to generate high-resolution occupancy grid maps for navigation. For autonomous operation, erosion processing and connected component labelling are used to define safe regions, while the A* algorithm enables efficient path planning and adaptive obstacle avoidance in complex environments. To detect unknown obstacles and environmental changes, Gaussian filtering and map differencing are applied, and map similarity is evaluated using histogram analysis and SIFT-based feature matching. Experimental results demonstrated that the system achieves a mapping resolution of 0.05 m and satisfies the Taiwan Association of Information and Communication Standards (TAICS) requirements, including 0.2 m planimetric accuracy and less than 0.1 m positional error. The proposed approach effectively identifies unknown obstacles and visually highlights risk areas, providing a reliable solution for intelligent workplace safety monitoring.

Centroid extraction from star images is a critical component in achieving high-precision satellite attitude determination. Prevailing approaches primarily focus on suppressing a single type of error or depend on fixed filtering and compensation parameters, often lacking a multidimensional and fine-grained analysis and handling of diverse error sources. To address these limitations, this paper proposes a compensation method for centroid extraction based on error classification and modeling, coupled with an adaptive strategy selection mechanism to improve accuracy. Experimental results demonstrate the efficacy of the proposed method: on a set of 30 to 300 laboratory-simulated star images, it enhanced the average centroid extraction accuracy from a baseline of 0.31–0.45 pixels to 0.11–0.19 pixels when using a Static model Unscented Kalman Filter (UKF) integrated with four sub-pixel interpolation techniques. Furthermore, for a larger dataset of 300 to 600 star images simulated at a 300 Hz frame rate, the method achieved an accuracy improvement exceeding 50% across five different motion model UKF methods, demonstrating robust performance.

In recent years, Space-Air-Ground sensing data has become increasingly abundant. This paper focuses on the technology system of integrated intelligent sensing in Space-Air-Ground remote sensing, aiming to integrate data from different platforms and sensor types through deep collaboration to meet the growing demand for high-precision, high-frequency, and near-real-time monitoring in scenarios such as land change detection, natural resource development, and disaster emergency response. This paper constructs a technical framework for the integrated intelligent sensing technology system for Space-Air-Ground remote sensing,focusing on core technical methods such as multi-source data governance and correlation, component-based AI interpretation model development, and the construction of application agents based on multi-modal large models.

This study validated the application of an integrated space-air-ground intelligent monitoring system through a typical ecological restoration project monitoring and supervision case. A test area of approximately 5.61 hectares in Hunan Province, China, was selected to construct an ecological restoration monitoring agent. This agent comprehensively utilized multi-temporal satellite imagery spanning eight years, UAV image data, and tower-based videos.Driven by natural language instructions, the agent autonomously planned task chains, coordinated multi-source data, and triggered models. After the implementation of the ecological restoration project, the results showed 4.87 hectares of new grassland and 0.74 hectares of new forest within the area, achieving intelligent identification and quantitative, automated assessment of land cover types, restoration progress, and ecological recovery outcomes. The experiment demonstrated the system’s advantage in "rapid identification and early warning",forming an intelligent operational closed loop of "monitoring-analysis-decision-feedback."

Traditional pavement inspection and data collection are often constrained by traffic conditions, operational safety, and equipment costs, making it difficult to achieve both efficiency and large-scale coverage. To address these limitations, this study employs a Pavement Roughness Index and Distress Extraction System (PRIDEs), which integrates high-resolution industrial cameras, high-precision global navigation satellite system (GNSS), wheel pulse sensors, and an onboard computer to acquire high-quality images under high-speed driving conditions. Using photogrammetry and computer vision techniques, camera poses are reconstructed to generate dense point clouds, digital surface models (DSMs), and orthophotos for detailed pavement distress analysis. However, the acquired imagery is affected by dynamic shadows and lens-focusing induced blur, resulting in ghosting artifacts and inconsistent orthophoto quality. To mitigate these issues, this study proposes a masking strategy during orthophoto generation, where U-Net is employed to detect shadow regions and Laplacian variance is used to identify blurred areas. By integrating these masks, more uniform and higher-quality orthophotos can be produced. Experimental results demonstrate that the proposed approach effectively reduces false positives and false negatives of crack detection caused by shadows and blur, thereby improving the reliability of orthophotos for automated pavement condition assessment.

Ultra-Wideband (UWB) technology is gaining

attention for indoor positioning due to its high accuracy, low

latency and resilience to interference, making it ideal for

environments where GNSS (Global Navigation Satellite System)

signals are unavailable—such as warehouses, hospitals, and

underground facilities. However, UWB systems can suffer from

reduced accuracy under Non-Line-of-Sight (NLOS) conditions

and dynamic deployments. This paper proposes a novel bias

aware EKF (Extend Kalman Filter) model, combined with

Anchor Self-Localization method for localization in indoor

environments, and enhancing the flexible deployment of

anchors. The proposed model demonstrates an overall

improvement of 32% and 41% in positioning accuracy

compared to traditional methods across both indoor and

outdoor environments respectively. The paper demonstrates the

proposed ASL method, it performs at par with conventional pre

calibrated methods where anchors are to be localized manually.

Together, the Bias-Aware filtering and ASL approach enhance

the scalability and reliability of UWB-based Indoor Positioning

Systems (IPS) for real-world applications.

Handheld 3D scanners have gained increasing importance in recent years due to their flexibility and declining acquisition costs. While high-end systems provide standardized accuracy specifications, affordable devices often lack reliable and comparable benchmarks. This paper evaluates the geometric accuracy of three low-cost handheld 3D scanners (Revopoint Pop 3 Plus, Revopoint MetroX, 3DMakerPro Moose 3D scanner) compared to two high-end systems (Hexagon MARVELSCAN, Hexagon Absolute Arm with AS1 scanner), using the ZEISS Atos 5 structured light system as reference. Five different test objects with varying material and geometric properties were used for practical assessment. Results reveal significant differences regarding flatness, detail fidelity, and robustness: while some low-cost scanners achieve remarkable accuracies, their performance is less stable under varying conditions. High-end systems, in contrast, consistently provide high precision and reproducibility. This study provides a well-founded classification of current handheld 3D scanners and practical guidance for their application in science, industry, and education.

In recent years, LiDAR-based localization has been widely explored. Among them, Scene Coordinate Regression (SCR)-based

methods have demonstrated outstanding accuracy and robustness in city scenes. Integrating these models with traditional Simultan

eous Localization and Mapping (SLAM) methods is expected to enhance localization accuracy and reliability further. This paper

proposes loosely coupled fusion methods integrating an SCR model with SLAM to improve localization accuracy and robustness.

The approach addresses the information loss problem in high-level sensor fusion while maintaining computational efficiency. The

method achieves tighter data association and complementary performance advantages by strategically combining LiDAR-based

localization results with SLAM pose estimates. Experimental results in the NCLT and HeLiPR datasets demonstrate that the pro

posed fusion framework effectively corrects SLAM drift and maintains stable pose estimation accuracy under diverse environmental

conditions. Furthermore, the sparse-frame coupling strategy significantly reduces computational overhead without degrading local

ization performance, making the method suitable for practical applications. The system exhibits improved robustness across regions

and LiDAR configurations while preserving real-time operation capabilities.

Matrix detector are becoming increasingly common in optical imaging satellite. To maintain good geometric quality of the images, the line of sight (LOS) of each pixel must be known precisely. This paper aims to estimate the performance of our method of LOS calibration on Co3D datas, which requires a precise geometric model for altimetric reconstruction.

In cold-climate regions like Canada, pavement infrastructure deteriorates rapidly due to extreme freeze-thaw cycles and heavy use of de-icing salts, accelerating the formation of structural cracks and imposing a financial burden on municipal budgets. By providing an automated LiDAR (Light Detection and Ranging)-based detection framework, this research offers a cost-effective, high-precision monitoring tool that enables early intervention, reducing long-term repair costs and enhancing road safety across Canadian provincial networks. This study evaluates the performance of Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs) for crack classification in a multi-dimensional feature space. We propose integrating geometric height (H) with a novel set of radiometric indices, including the Normalized Difference Intensity Index (NDII) and the Green Ratio (GR), to enhance classification stability. Results demonstrate that both SVM and MLP achieved comparable accuracies of 87% and 86%, respectively, in low-dimensional feature spaces. A critical analysis of the MLP learning curves reveals that the introduction of NDII acted as a numerical stabilizer, mitigating the oscillations caused by raw brightness fluctuations. Furthermore, the study identifies an information ceiling, as architectural expansion of the MLP improved convergence stability but did not exceed the 87% accuracy threshold. These findings provide a robust framework for automated road maintenance using stabilized radiometric features in LiDAR-based distress identification.

The aim of the study was to propose a procedure enabling accurate mapping of the above water and underwater areas of the bridge. The object of the study was a road bridge located approximately 30 km north of Warsaw, Poland. The bridge is 332 m long and 13.5 m wide. The bridge is located over Lake Zegrze. A mobile topographic Norbit iLIDAR system was used to measure the bridge structure above the water surface. Bridge pillars measurement and the shape of the bottom of the water reservoir in the immediate vicinity of the bridge were performed using a Norbit Winghead i77h multibeam echo sounder. In order to bridge point clouds integration, a workflow has been proposed: LIDAR and MBES data filtering, consideration of the speed of sound in water, LIDAR and MBES data calibration including Patch Test and MBES cross check. As a result, the integrated point cloud of the bridge was created. The LIDAR point cloud resolution was 1 cm and the MBES point cloud resolution was 0.02 m. The created point cloud of bridge provides could be useful for monitoring erosion and accumulation phenomena, analyzing the stability of bridge pillars and verifying hydrodynamic models.

Accurate characterization of the terrestrial laser scanner (TLS) beam footprint is essential for understanding the stochastic behavior of the scanner. Estimating the laser footprint width is crucial for determining the correlation length between neighboring observations, and thus for providing a realistic estimation of the correlation elements within the variance-covariance matrix of the measurement uncertainties. In this contribution, an intensity-based workflow is introduced to estimate the laser beam footprint by deriving the line spread function (LSF) from the edge spread function (ESF). The proposed method applies no assumptions regarding the geometric or physical behavior of the beam, allowing the footprint to be determined directly from the measured intensity values. Using the BOTA8 target, the Z+F Imager 5016A was investigated at two different distances and two scanning rates with varying mirror rotation speeds. The results provide insights into the influence of distance and scanning rate on the laser beam footprint.

UAV-borne PV point clouds often suffer from severe shadow artifacts and color dropouts, limiting their use for reliable inspection and digital twin construction. We introduce a fully vectorized color rectification framework that exploits panel symmetry and 1D signal processing to restore a consistent radiometric appearance. Starting from a segmented solar-panel point cloud, the method first normalizes panel geometry via RANSAC-based plane segmentation and rotation to a canonical xy-plane, then extracts base colors by clustering RGB values to identify “panel blue” and “grid white” regions. It subsequently detects grid parameters by projecting filtered grid and panel points into 1D spatial density histograms along the x- and y-axes to estimate spacing, offset, and grid-line thickness, and finally performs vectorized recoloring and color remapping of grid and panel points using the recovered parameters. By decoupling periodic grid structure from illumination noise, our approach achieves visually near-perfect color restoration while eliminating intra-semantic variance across modules. The resulting high-fidelity, shadow-free point clouds provide a mathematically consistent foundation for PV digital twins and automated asset evaluation.

EarthDaily Constellation (EDC) is a ten-satellite, sun-synchronous mission optimized for persistent, daily monitoring of global land and designated coastal waters. Each spacecraft carries co-aligned VNIR, SWIR, and TIR imagers and acquires nadir-only imagery at ~10:30 LTAN to stabilize collection for optimal change detection. A systematic acquisition plan builds a global spatiotemporal archive; EarthPipeline performs automated geolocation, orthorectification, atmospheric correction, QA, and wide-area compositing. Bands and metadata are designed for CEOS CARD4L-SR alignment and inter-sensor interoperability with Landsat and Sentinel-2. The talk reports early on-orbit performance—geometric accuracy, radiometric stability—and benchmarking of atmospheric correction and cloud/shadow masking against ESA Sentinel-2 processing, with a focus on time-series consistency for analytics and ML. We also outline specialized applications concepts and readiness.

This paper presents a static calibration approach for lightweight laser scanning systems, utilising planes as control entities, with a focus on estimating boresight misalignment angles. The calibration with the system static, aims to minimise errors originating from several sources, such as position and attitude systems, time synchronisation, and control features measurement. The mathematical model is based on the plane equation, combined with the equations of laser scanning. The estimation is performed with the combined model of least squares. Experiments in a terrestrial calibration field were performed. The results show that the approach successfully estimates the boresight misalignment angles, reducing the errors of the point cloud with respect to the control planes.

Monitoring water resources is essential for supporting human activities and enabling informed decision-making. Since its launch in 2022, the Surface Water and Ocean Topography satellite mission has provided global observations of surface water elevation for rivers, lakes and oceans. Several studies have evaluated SWOT performance for ocean applications (Hay et al., 2025, Lichtman et al., 2025) and continental water bodies (Patidar and Indu, 2025). However, no comprehensive assessment has yet focused on Canadian inland waters. This research presents an initial evaluation of SWOT water surface elevation observations using hydrometric stations operated by Water Survey of Canada (WSC). This evaluation covers the period between operational orbit reached in July 2023 and December 2025.

This presentation introduces a novel approach for high-resolution Earth observation using GNSS-SAR imaging with spaceborne GNSS-Reflectometry satellites. By leveraging low-level intermediate frequency (IF) signals from the CYGNSS satellite constellation, our work demonstrates the feasibility of forming GNSS-SAR images from spaceborne GNSS-R data. The integration of advanced weak signal tracking algorithms and tailored SAR image formation techniques enables the retrieval of Earth observation data with unprecedented spatial and temporal resolution. This addresses longstanding challenges in space-based GNSS-R remote sensing, such as limited spatial resolution and weak signal reception. The LEO satellite-based GNSS-SAR approach offers significant advantages, including global coverage, rapid revisit times, and the potential for onboard processing. These features collectively support scalable, near real-time monitoring of dynamic Earth processes, making this technique highly relevant for extreme weather surveillance, disaster preparedness, and environmental monitoring.

This study presents a low-cost LiDAR–IMU–GNSS mapping framework for continuous and globally consistent three-dimensional reconstruction across indoor–outdoor environments. The work addresses a key limitation in current SLAM and GNSS-integrated systems, where LiDAR-based approaches provide strong local geometric accuracy but lack reliable global referencing, while GNSS-based solutions often rely on high-precision corrections such as RTK or PPP, limiting scalability and deployment in urban environments.

Building upon the Dense Multi-Scan Adjustment SLAM (DMSA-SLAM) framework, the proposed system introduces a structured integration of standalone Single Point Positioning (SPP) GNSS through an external alignment strategy, ensuring that global referencing is achieved without compromising locally consistent LiDAR–Inertial geometry. The framework further incorporates explicit multi-level structural constraints to support consistent cross-floor reconstruction, along with a bounded optimization and loop closure strategy that maintains stability and prevents global trajectory deformation without requiring full pose graph optimization.

The system is validated in a multi-storey urban building under challenging GNSS conditions, including complete signal outages and urban canyon effects. Results demonstrate sub-decimeter indoor geometric accuracy and meter-level global georeferencing using low-cost sensors. Comparison with a high-accuracy terrestrial laser scanning (TLS) reference confirms reliable reconstruction quality, while the proposed system achieves rapid mapping in a single continuous trajectory using a significantly lower-cost sensor suite.

Overall, the framework provides a practical and scalable solution for infrastructure-free indoor–outdoor mapping, supporting applications in BIM, digital twins, and urban asset management.

This contribution presents a comprehensive calibration framework for a hybrid solid-state LiDAR system designed for small body exploration. Integrating imaging modeling, photon-count–based parameter estimation, and multi-scale ground experiments, the method effectively corrects pixel-dependent range and angular errors. Rigorous validation demonstrates centimeter-level accuracy in both mapping and navigation modes, confirming the framework's robustness and its critical role in enhancing deep-space mission capabilities.

This research introduces a deep learning-based, multi-sensor framework for automated damage detection in cultural heritage structures using fused thermal and optical imagery. Conducted across five historic sites in South Delhi, India, the study targeted common degradation forms—cracks, spalling, and biological growth—through high-resolution image acquisition using a FLIR T1030sc thermal camera and RGB sensors. Fused datasets (MXS and thermal-optical blends) significantly outperformed optical-only inputs, with the YOLOv11-Tuned model achieving a peak mAP of 91.8%. The fusion allowed reliable detection of subsurface anomalies and fine-scale damage often missed by traditional visual inspections. Oriented Bounding Box (OBB) variants improved localization of non-linear features, while genetic algorithm-based hyperparameter tuning enhanced model precision. The framework offers a scalable, non-invasive, and accurate alternative to manual inspection, supporting early diagnostics and long-term conservation planning. This approach demonstrates the transformative potential of AI and remote sensing in preserving architectural heritage against both environmental and anthropogenic threats.

Hyperspectral data alone in mineral exploration often suffers from limitations including signal noise, coarse spatial resolution, and spectral variability, which can hinder mineral discrimination. To address these challenges, we integrate Short-Wave Infrared (SWIR) and Visible Near-Infrared (VNIR) hyperspectral data cubes with complementary sensor modalities, including RGB imagery and LiDAR acquired from indoor scans of drilled core. This multi-sensor fusion enhances the reliability and accuracy of mineral maps by leveraging the strengths of each modality.

At GeologicAI, our indoor scanning platform captures multi-modal data from a box of core using a variety of different sensors. A critical preprocessing step involves isolating the drilled core from the background. We further applied a continuous wavelet transform (CWT) for a scalogram analysis enables the differentiation of unclassified spectra based on their frequency-scale characteristics. Following spatial masking and unclassified spectral filtering, we apply a local end-member selection regime utilizing RGB, VNIR and SWIR for all valid pixels. Afterwards, non-negative least squares (NNLS) linear unmixing. While SWIR remains the primary source for mineral identification and abundance calculations, VNIR and RGB data provide critical support in resolving ambiguities either confirming the presence of minerals difficult to detect with SWIR alone or excluding candidates based on VNIR disagreement or RGB colour disagreement. Mineral maps derived from SWIR data exhibit a reconstruction residual error of 12.4%. While the integration of VNIR data does not necessarily reduce this residual, it enhances confidence in abundance estimations, particularly in regions where SWIR alone cannot separate end members.

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Calibration of terrestrial laser scanners (TLS) is paramount for ensuring high-precision measurements. The costs and efficiency of calibration pose significant challenges for both instrument manufacturers and end-users conducting self-calibration of TLS systems. To date, there has been a lack of theoretical methods for quantitatively analyzing and optimizing the geometric network of targets within calibration fields. This study proposes the TNet-GDOP (Target Networks Geometric Dilution of Precision) theory and its mathematical model to quantitatively evaluate the impact of target distribution on parameter solution precision. We propose the optimized the target network strategy based on the precision contribution factor of TNet-GDOP (OptimizeTNet-PCF), a target distribution optimization algorithm with a well-defined scoring function. OptimizeTNet-PCF can reduce the number of targets with minimal effect on parameter precision while suppressing anomalous observations. The number of targets was reduced to one-eighth (from 140 to 16), with ranging parameter variations less than 0.1 mm and angular parameter variations less than 0.2″.

This paper examines the feasibility of calibrating a consumer camera with a calibration panel to accurately reconstruct seabeds in shallow water. Specifically, it assesses whether calibration parameters determined based on the panel can be applied to an independent set of images captured under different conditions. The study also examined the effect of the analyzed approach on the final accuracy of the point cloud. The analysis covered three calibration variants: (1) external calibration based on an underwater panel, (2) preliminary calibration in which the panel parameters were used as initial values for further optimization, and (3) fully automatic autocalibration. The results showed that calibration using the panel does not improve reconstruction quality and can lead to model distortion. The highest accuracy was achieved with in situ autocalibration, supported by underwater control points.

The Advanced Land Observing Satellite-4 (ALOS-4), launched on July 1, 2024, observes the Earth's surface using its onboard Phased Array type L-band Synthetic Aperture Radar (PALSAR-3). Japan has continuously advanced L-band radar technology, and ALOS-4 offers significantly improved observation performance compared to its predecessor, PALSAR-2, aboard ALOS-2, which was launched on May 24, 2014. ALOS-4 is designed to achieve both high spatial resolution and a wider observation swath—expanding the 3 m strip map mode coverage from ALOS-2’s 50 km to 200 km.

By employing this wide-swath observation capability, ALOS-4 can acquire 3 m dual-polarization data over Japan approximately once every two weeks. These frequent observations support disaster management by providing timely information on events such as volcanic activity, land subsidence, and landslides. Moreover, the high-temporal-resolution 3 m dual-polarization data are valuable for a wide range of applications, including agriculture, ocean monitoring, and environmental studies.

To effectively utilize ALOS-4 data, it is essential to integrate it with the long-term archive of ALOS-2 observations, enabling time-series change detection. Maintaining consistent geometric and radiometric quality between ALOS-2 and ALOS-4 data through cross-calibration and validation is therefore critical. This paper presents the results of these efforts and outlines the current use of ALOS-2 and ALOS-4 data under the ALOS-2 Public–Private Partnership (PPP) Phase B activities.

This study investigates how differences in UAV-LiDAR sensor performance affect the accuracy of canopy radiative transfer simulations. Conducted in a Japanese larch forest in Yamanashi, Japan, the research compares two UAV-mounted LiDAR systems—YellowScan Explorer and Voyager—flown over the same plot. The simulation approach uses a voxel-based model to estimate solar irradiance attenuation and reflection, optimizing parameters to match Sentinel-2 NIR reflectance.

Results show that Voyager, which produced over twice the point density of Explorer, achieved a higher correlation with Sentinel-2 data (r = 0.74 vs. r = 0.67). This suggests that higher point density improves upper-canopy representation and enhances simulation accuracy. However, the study also emphasizes the continued importance of complementary ground-based LiDAR (e.g., handheld or TLS) for capturing understory structure.

The findings highlight that UAV-LiDAR is essential for accurate canopy modeling, but sensor specifications—particularly point density—significantly influence radiative transfer outcomes. Future work should explore integrating multiple LiDAR sources and testing scalability across diverse forest types and phenological stages.

Soil erosion is a significant threat to ecosystem quality, and the development of accurate models to map erosion susceptibility is essential for enhancing public mitigation policies. This study investigates the applicability of the algorithms Weighted Subspace Random Forest (WSRF), Random Rotation Forest (RRF), and Naive Bayes (NB) to map soil erosion susceptibility in the Rio Pardo watershed, located between the states of São Paulo and Minas Gerais. A total of 120 sample points of erosion and non-erosion sites were used, identified through high-resolution images from Google Earth Pro and field visits. Fifteen conditioning factors were initially considered, but after analyzing multicollinearity and factor relevance, only thirteen were selected for the final modeling. The dataset was randomly divided into 70% for training and 30% for testing to assess the robustness of the models. The performance of the algorithms was evaluated using metrics such as accuracy and AUC-ROC. The accuracies obtained were 0.87 for NB, 0.89 for RRF, and 0.88 for WSRF, while the AUC-ROC values were 0.93, 0.96, and 0.95, respectively. RRF showed the best performance, confirming the usefulness of these models in sustainable management and conservation of areas susceptible to erosion.

This work combines in situ measurements of near-surface air temperature with the CMCC VHR-REA_IT climate reanalysis dataset to assess the spatial and temporal dynamics of heat wave (HW) events and evaluate heat stress (HS) conditions across Italy for the period 1981-2024. HWs are characterised in terms of their frequency, duration, and intensity, while HS is evaluated through ad-hoc indices, including Humidex. A trend analysis is performed to investigate the temporal trends of HWs and of hazardous HS conditions. Results indicate a significant increase in the number of HW events alongside a growing frequency of severe thermal discomfort conditions (up to 6 days more per decade). Overall, this work underscores the intensification of heat-related hazards in the study area, emphasising the need for mitigation and adaptation strategies. The ultimate goal is to develop a scalable, open-source methodology that enables continental-scale assessments of heat extremes and their impacts.

Current Historical Geographic Information (HGI) research faces significant challenges in integrating multi-source heterogeneous data (China Historical GIS Project, 2025). The lack of unified semantic standards, effective interoperability mechanisms, and systematic organization of historical sources has led to severe "data silos." Consequently, a core problem remains: the semantic fragmentation, temporal inconsistency, and disconnected evidence chains of complex cultural landscape data (Southall, 2014). While existing approaches successfully utilize traditional GIS for spatial management or foundational ontologies (e.g., CIDOC CRM) (Bekiari et al., 2024) for static artifact cataloging, they struggle to formalize and compute the dynamic evolution of heritage sites over long historical trajectories.

To overcome these bottlenecks and advance the multidimensional application of cultural landscape heritage data, this study proposes a data organization framework centered on semantic normalization and standardization. Driven by a novel hybrid semantic architecture, we construct an extensible semantic foundation and a multi-source fusion mechanism. This approach seamlessly couples macroscopic cultural landscape heritage event-centric modeling with microscopic temporal annotations, strictly regulated by a "policy–ontology–rules" constraint mechanism.The framework is designed to support computable, searchable, and inferable unified knowledge representations, thereby enabling deep integration of spatio-historical big data, semantic reasoning, and evidence- based decision-making for cultural landscape heritage management.

This presentation addresses our profession’s need for new methods to identify rapidly the prominent patterns concerning the locations, time intervals, classes, and transitions that account for gross changes during sequential time intervals in a series of maps, as opposed to popular methods that compute merely the sizes of classes at time points. Trajectory Analysis is a method that computes various components of change during a time series for exactly one land cover class. Our method of Change Components Analysis extends the concepts of Trajectory Analysis to present new concepts to address multiple classes using our new free software. Our novel methods are especially effective at identifying where, when, which classes, and which transitions demonstrate suspicious changes that warrant attention to data quality. Our new methods identify also change components that can give insights to landscape processes.

Spatial association reveals the interconnected nature of geographical phenomena, describing the interactions and influences of environmental variables across geographic space. Path analysis can explore complex causal relationships between variables by analyzing path coefficients. However, in large-scale studies, path analysis methods are often affected by local effects, which can influence the accuracy and reliability of the results. This study develops a local pathway association (LPA) model to analyze local effects of pathways among variables that integrate path analysis and local pathway coefficient estimations. The LPA model was employed to investigate the spatial heterogeneity of spatial associations between factors such as climate, soil, and vegetation on the Tibetan Plateau. Results indicate that the LPA model effectively reveals the spatial variation characteristics of local path coefficients between geographic variables, avoiding the underestimation or overestimation of global path coefficients in traditional path coefficient studies. The developed LPA model provides an effective technical tool for revealing spatial differences in path associations of large-scale spatial studies. The strong data compatibility of the LPA model allows for broad applicability across various disciplines and a deeper understanding of localized interactions and variations in complex geospatial and Earth systems.

This Contribution presents a generative Flow Matching Framework for sub-daily Wildfire Progression Monitoring from combined MODIS, VIIRS and Sentinel-3 Observations. The Approach treats all available Multi-Sensor Looks as irregular Samples along an underlying Spatio-temporal Fire Trajectory and learns continuous Vector Fields that map coarse Reflectance Observations to Sentinel-2-like Reflectance and Burned Area Masks. The Input Constellation uses MODIS Bands 1, 2 and 7, VIIRS I1-I5 and Sentinel-3 OLCI Bands Oa08 and Oa17 together with SLSTR Band S6, providing complementary Information in the visible, NIR, SWIR and Thermal Domains as well as staggered Overpass Times. Labels are derived from Sentinel-2 Surface Reflectance and Burned Area Polygons from the National Burned Area Composite as well as additional manually interpreted Fire Perimeters. We expect the learned Model to reconstruct Fire Progression at 3-6 Hour Resolution for many large Events, to improve Burned Area Delineation over single Sensor Baselines, and to provide Ensemble-based Uncertainty Estimates that highlight ambiguous Regions under Smoke or Cloud. The resulting Multi-Sensor Dataset and trained Model are intended as reusable Resources for future Research on Wildfire Monitoring and Data Assimilation.

This text outlines an approach to achieving practical geospatial data interoperability through incremental, data-driven standardization rather than relying on a single, universal standard. It frames interoperability as an evolving process in which data models, syntactic formats, semantic vocabularies, and protocol bindings are progressively aligned, generating network effects that lower implementation costs. The AMPLIFY-EDS project applies these principles to the end-to-end lifecycle of Near Real Time (NRT) environmental sensor data across the UK Environmental Data Service (EDS). Led by the British Oceanographic Data Centre (BODC), the project establishes a federated API ecosystem using the OGC SensorThings API (STA), integrating multiple MQTT data streams from research vessels and partner data centres. A Python relay application performs ingestion, validation, and quality control before posting data to a FROST server, while a React frontend provides visualisation. Metadata harmonisation required community agreement on minimal entity requirements, vocabularies, and JSON schemas, drawing on schema.org and SOSA. The team then enriched STA outputs by mapping JSON to JSON-LD and creating context files validated through OGC Building Blocks.

This study presents a spatiotemporal modeling framework for predicting hourly NO2 concentrations in Seoul by incorporating dynamic vehicle activity data recorded from Digital Tachographs (DTG). Conventional Land Use Regression (LUR) models rely on static spatial predictors and therefore struggle to represent short-term emission dynamics driven by rapidly changing traffic conditions. To overcome this limitation, this research integrates high-frequency DTG variables—vehicle speed, acceleration, braking events, and truck activity—into a dynamic LUR model and evaluates hourly NO2 variability across the urban environment.

Model performance was assessed using panel regression with random effects and hourly time indicators to capture temporal fluctuations at fixed monitoring locations. The DTG-integrated model exhibited substantially improved explanatory power, raising the within R2 from 0.17 in the static baseline to 0.25. The consistent significance of DTG-derived predictors highlights the dominant influence of real-time traffic behavior on short-term pollution levels and confirms the value of incorporating high-resolution mobility data.

Hourly prediction maps revealed strong diurnal patterns, with concentrations lowest at 4 a.m. and highest at 8 p.m., when evening congestion produced values nearly double those of early morning. A LISA cluster analysis further showed that high–high spatial clusters expanded from 17% to 28% of the study area during peak hours, demonstrating increased spatial concentration of pollution. The transition of grid cells between cluster categories also indicated dynamic shifts in spatial patterns throughout the day.

Overall, this study demonstrates that integrating DTG data substantially improves the characterization of hourly pollution dynamics and provides a foundation for time-sensitive, location-specific air-quality management strategies.

This research presents a new Multi-LoD4 Application Domain Extension (ADE) for CityGML to improve the integration of Building Information Modelling (BIM) and Geospatial Information Systems (GIS) in Urban Digital Twins (UDTs). The proposed approach extends CityGML’s Level of Detail concept to better represent both exterior and interior elements of buildings while keeping their semantic information complete. It links the geometric model to a graph-based database that stores and connects all building components, allowing users to visualize and query the data interactively in a web environment. The Multi-LoD4 ADE enhances interoperability, semantic richness, and data accessibility, providing a more comprehensive and practical foundation for future UDT applications in areas such as building management and urban analysis.

This work presents an automated and adaptive pipeline for detecting photovoltaic (PV) systems in high-resolution satellite imagery. The solution was developed to support large-scale energy monitoring efforts in the state of Minas Gerais, Brazil, where geographic diversity and visual variability pose significant challenges to accurate PV identification. The proposed pipeline operates from a single pair of geographic coordinates, automatically defining the area of interest, acquiring a basemap image, classifying the spatial context through HSV histograms, UMAP dimensionality reduction, and K-Means clustering, and dynamically selecting the most suitable deep learning segmentation model. Multiple U-Net architectures with different ImageNet-pretrained encoders were evaluated to segment PV panels, and building footprints from public datasets were used to refine detections through geospatial segmentation (SamGeo). Experimental results indicate that model performance varies across environmental contexts, highlighting the importance of context-aware model specialization. Preliminary evaluations show that dynamically assigning models such as ResNet50 and VGG16 to their optimal clusters improves segmentation accuracy. Overall, the proposed methodology demonstrates a modular, scalable, and context-adaptive approach for PV system detection, suitable for integration into urban and energy monitoring platforms.

Clustering methods are widely used in regionalisation research to identify spatial and functional structures within complex economic systems. Yet different clustering specifications can lead to contrasting interpretations of regional patterns. Advanced Producer Services (APS), i.e., specialised, knowledge-intensive business services, provide a useful setting to examine these methodological choices.

This paper develops a framework comparing spatially constrained and unconstrained clustering for delineating APS employment regions in the UK. Spatial methods group neighbouring units to preserve geographic contiguity, while non-spatial methods group areas with similar employment profiles regardless of location. We ask to what extent APS regionalisation follows spatial contiguity versus functional--economic linkages that transcend geography.

Our contribution is twofold. Substantively, we show that APS in the UK form functionally coherent but spatially fragmented regions, challenging planning approaches that assume contiguous blocks of territory. Methodologically, we quantify the trade-off between cluster quality and spatial interpretability, providing a simple diagnostic to guide method choice in regionalisation studies.

Effective disaster response requires rapid allocation of limited human and material resources to dispersed and dynamically changing demands. This study formulates a regional travel problem, an extension of the Multiple Traveling Salesman Problem (mTSP), to optimize the assignment and routing of responders—such as firefighters and volunteers—to affected individuals and facilities. To address the NP-hard nature of the problem, a computationally efficient heuristic is proposed that integrates fuzzy c-means clustering and a genetic algorithm (GA). Responders are first stochastically assigned to demanders based on a composite score combining distance, compatibility, and urgency. Remaining demanders are then optimally allocated using a GA to minimize total travel completion time while balancing workload. The model incorporates three key factors—workload differences, responder–demander compatibility, and urgency—and is implemented as a web-based travel assistance application capable of real-time recalculation when new responders or demanders appear.

Simulation experiments conducted in Setagaya Ward, Tokyo, demonstrated that accounting for workload differences and enabling dynamic recalculation significantly reduced completion time and improved cooperative task efficiency. Field experiments with actual responders verified these findings: the proposed system halved total completion time compared to conventional SNS-based coordination and eliminated route overlaps and missed visits. The results confirm that the proposed model and system enhance operational efficiency and reliability in dynamic disaster environments. This research provides a practical, data-driven foundation for real-time disaster management, with future work focusing on scalability, responder performance calibration, and robustness under disrupted network conditions.

Hyperparameter tuning is a critical step in training artificial intelligence (AI) models for Earth observation (EO) tasks, as it directly impacts model accuracy, convergence speed, and generalization capacity. Traditional optimization methods such as grid search, random search, and Bayesian optimization often suffer from high computational costs and limited scalability, particularly when applied to complex model architectures and large datasets. Grid and random search scale poorly with dimensionality of the search space and often waste evaluations on unpromising regions of the search space, especially for deep neural networks. Random search improves over grid search but still requires a large number of trials to reliably find good configurations in high-dimensional search spaces. Bayesian optimization methods, while more sample-efficient, typically involve non-trivial surrogate modelling and acquisition optimization steps that add overhead and can struggle with very large, mixed (discrete–continuous) search spaces. These challenges are further amplified in EO applications, where segmentation models are trained on large datasets, making each hyperparameter evaluation computationally expensive and limiting the practicality of purely classical search strategies.

Recent advances in quantum computing have introduced novel paradigms for solving combinatorial optimization problems. Quantum-inspired and hybrid quantum-classical algorithms leverage principles such as superposition and probabilistic amplitude encoding to enhance search efficiency in high-dimensional spaces while benefiting from the strengths of classical algorithms. Building on these concepts, we investigate a Hybrid Quantum Genetic Algorithm (HQGA) for hyperparameter tuning. To evaluate this approach, we apply it to the optimization of a semantic segmentation model specialized for wildfire burnscar detection.

This study presents a two-stage framework for planning Electric Vehicle (EV) Charging Infrastructure that explicitly targets Spatial Equity on Jeju Island. First, projected 2035 Origin–Destination demand is downscaled to a 500 m grid and evaluated on a routable road graph using a network-based Gaussian Two-Step Floating Catchment Area (G2SFCA) model to produce a high-resolution Accessibility surface. Second, a Quadratic Programming (QP) model jointly optimizes station Location and charger Capacity under a fixed budget by minimizing the demand-weighted variance of Accessibility, thereby reducing disparities across demand cells. Candidate stations are derived from publicly accessible Points of Interest and selected with a coverage-oriented clustering scheme; a greedy loop adds sites that yield the largest marginal reduction in the equity objective, with capacities re-optimized by QP at each step.

The evaluation compares four scenarios—status quo, Location-only, Capacity-only, and the proposed joint optimization—using established equity metrics including the demand-weighted Standard Deviation, Mean Absolute Deviation, Coefficient of Variation, and the Gini coefficient. Although full numerical results are in progress, preliminary simulations indicate that the joint strategy delivers more balanced Accessibility across urban and rural areas than single-focus baselines while maintaining overall service levels. The framework is reproducible, policy-oriented, and transferable to other regions, offering planners a rigorous, data-driven tool to allocate limited public fast-charging resources fairly under future EV uptake.

The “last meter” dilemma in urban accessibility refers to the lack of accessible information at the terminal points of public service facilities, which hinders wheelchair users' mobility, even physical infrastructure may be present. This study investigates this dilemma on a global scale, analyzing over 210,000 parks across 100 of the world's most populous cities to quantify how information gaps create real-world barriers.

To quantify these gaps, the study introduces two metrics: Absolute Accessibility Loss (AAL) and Accessibility Gap Ratio (AGR), which measure the additional travel time burden on wheelchair users resulted from the lack of accessible information. The findings show that only 34.9% of parks are labelled as accessible. This disparity has tangible consequences: Wheelchair users must travel farther and spend more time reaching parks labelled as accessible than the general population does to reach any park. The study also reveals a clear global divide, where high-income cities show higher labelling rates and shorter travel times for wheelchair users, while cities in Africa, India, and Southeast Asia exhibit higher disparities

This study furnishes a framework for policymakers, presenting a novel perspective for the assessment of urban equity and a scalable instrument for tracking advancements towards the United Nations Sustainable Development Goals, specifically SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities).

Image geo-localization aims to infer where a photograph was taken purely from its visual content. This task underpins applications in navigation, urban analytics, disaster response, and environmental monitoring, but current vision-language models (VLM) are mostly trained on generic web data with little explicit geospatial information. This work develops GeospatialCLIP, a geospatially enhanced VLM that embeds geospatial intelligence directly into CLIP via spatially explicit contrastive learning.

GeospatialCLIP is trained on 180k geotagged image-text pairs spanning street-view imagery, multi-temporal satellite images (2014 and 2023), and OpenStreetMap tiles. Rich captions and spatial context are curated by GPT-4 and experts, describing spatial patterns of objects, land use, urban form, and features that support geo-localization. A spatially explicit text encoder integrates structured tokens with geo-image type and geo-location across scales, enabling a shared geospatial representation space.

Zero-shot global geo-localization experiments evaluate GeospatialCLIP on unseen datasets across geo-locations, scales, and years, and compare it with vanilla CLIP and ResNet backbones. Across city, country and continent levels, GeospatialCLIP consistently improves top-1 accuracy for all imagery types, and its zero-shot performance on street-view images matches few-shot CLIP. The results highlight how embedding geospatial knowledge into VLMs can yield more robust, data-efficient GeoAI models and point towards future geospatial foundation models that better support scientific discovery and real-world decision-making.

Tourism and geographic text data is one of the most common data types in spatial analysis, and the classification of such data is an essential preprocessing step to facilitate more in-depth mining of spatial-temporal information. In the past decade, a variety of classification methods for tourism and geographical text data have been developed. These methods established important foundations for automated text analysis, yet their effectiveness has often been constrained by the availability of labelled data and the need for carefully designed feature representations. Recently, large language models (LLMs) demonstrate clear advantages in long-sequence modeling, offering new directions for text classification, particularly for long-form texts. However, employing commercial LLMs poses a significant cost challenge due to the high expense per token, and processing long texts consumes a considerable volume of tokens. In fact, it is feasible to adopt a strategy of locally deploying and fine-tuning open-source large language models that have reduced parameter counts. In this study, we have trained some open-source LLMs with chain-of-thought text. Experimental results show that the highest-performing model (e.g. fine-tuned Qwen3-1.7B) achieves an average accuracy of 95.83%, improving by 4.17% over the baseline RoBerta. Classification results can support tasks such as intelligent tourism recommendations, geographic knowledge construction, and toponym recognition. It may be concluded that the proposed chain-of-thought-guided LLM method can be effectively employed to classify tourism and geographic text data, and LLMs with reduced number of parameters have the potential to solve specific tasks with limited computation resources.

High spatio-temporal resolution estimation of XCO₂ is crucial for accurately quantifying regional carbon sources and sinks. Because XCO₂ variability is influenced not only by local geographic conditions but also by surrounding environmental and meteorological factors, this study proposes an advanced estimation approach that fuses multi-scale spatial features. We develop SpatialFusionNet, a convolution-based module that leverages local spatial association and receptive-field characteristics to integrate meteorological and surface environmental information within a 2.3° × 2.3° grid. This module extracts and fuses spatial feature patterns and subsequently estimates XCO₂ concentrations.

By combining SpatialFusionNet with machine learning methods—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Deep Neural Networks (DNN)—we construct a deep spatial-feature fusion model based on OCO-2 XCO₂ observations over China, CAMS reanalysis data, meteorological variables, and vegetation indicators. Significant performance improvements are achieved: RMSE decreases by 1.297 ppm (SVM), 0.480 ppm (DNN), and 0.200 ppm (XGBoost) in ten-fold cross-validation against OCO-2 trajectory samples. Validation using the TCCON Hefei station yields a correlation of 0.85, demonstrating strong reliability.

Using the DNN combined with SpatialFusionNet, we further generate a seamless annual XCO₂ distribution for China in 2015 and analyze its temporal–spatial characteristics. The proposed framework provides an effective pathway for producing high-resolution XCO₂ datasets and supports fine-scale assessment of regional carbon cycling.

Urban strategies establishing climate shelters typically delineate service areas using 15-minute walking isochrones, aligning with "chrono-urbanism". However, this practice often relies on the standard walking speed of a healthy, middle-aged male, a simplification that risks significantly overestimating the real accessibility for vulnerable groups, such as the elderly.

This paper presents a dynamic methodology to analyse how accessibility changes when accounting for two crucial factors: age/gender and thermal comfort (heat exposure along the route). The approach uses the Physiologically Equivalent Temperature (PET) index to dynamically adjust walking speed based on environmental conditions and the heightened vulnerability of subjects (represented by a 65-year-old female).

Applied to a case study in Turin, Italy, the results demonstrate a profound accessibility error caused by standard methods. Neglecting the combined effects of age and heat may lead to a 100% overestimation of the actual number of elderly women served. When these factors were integrated, the municipal area covered by shelters plummeted from 35.2% (standard scenario) to only 8.6% (highest stress scenario). Furthermore, the proportion of elderly women considered served dropped drastically from approximately 65% to just over 18%.

These findings confirm that dynamic accessibility calculations are essential for identifying optimal locations for new climate shelters and ensuring effective, equitable adaptation strategies.

This study aimed to develop an easily accessible, interactive digital twin model in Unreal Engine that visualizes urban trees and their carbon flux based on the Metsäkanta tree database, and

simulated carbon sequestration and emissions dataset. The model provides a flexible and automated framework for incorporating additional carbon and tree data for any area. Additionally, it showcases the potential of data-driven game engine visualizations in creating engaging scientific communication for a broader demographic.

This contribution presents an exploratory study on the relationship between virtual and physical trajectories in London, with a particular focus on how their spatio-temporal lags evolve under different urban conditions. Virtual trajectories are derived from map tile access logs of OpenStreetMap, while physical trajectories are constructed from anonymised mobile phone data. Both datasets are aggregated to Middle Layer Super Output Areas (MSOAs) for the period from 1 February to 30 April 2020.

We apply a simple rolling-window cross-correlation to each MSOA to monitor, over time, whether virtual activity leads, coincides with or lags behind physical activity. Two case studies illustrate the insights provided by this spatio-temporal lag detection. Around major concerts at the O2 Arena, virtual trajectories consistently lead physical trajectories by approximately 1–3 days, reflecting anticipatory route planning and information searches. Around the first Covid-19 lockdown, the lag landscape reorganises: positive lags become more dominant and their spatial configuration shifts, indicating that virtual activity remains a robust leading signal for constrained but persistent urban mobility.

A Wikipedia-style collaborative mapping website is proposed in this paper to document, to archive, and to share these TEK. All knowledge articles are contributed by volunteers based on the volunteered geographic information (VGI) concept. The article can be written in the corresponding indigenous language to precisely describe their cultural knowledge. Compare to the Wikipedia, this website is actually a WebGIS. A knowledge article refers to a point, a polyline, or a polygon, which means the knowledge article is georeferenced. The website is composed of open software, such as MySQL, OpenLayers, GeoServer, Drupal and Apache. These indigenous knowledge articles are the source of contents of the Virtual Tribe, the virtual reality of their home tribe. We deployed UAV to take aerial photographs and produced ortho-rectified images and 3D mesh models of the tribe. We also applied 360°panorama camera to take 360°panorama images or videos at important locations when we walk with the elder people around the tribe. Finally, these images, 3D model, and TEK are integrated in the virtual tribe. It’s like a digital twin of the home tribe. Users can explore the tribe and learn TEK from elder people who speaks indigenous language in the 360° panorama video embedded in the virtual tribe.We have cooperated with two high schools in the indigenous countries to build up an immersive virtual reality (iVR) using the TEK articles on the proposed website. The feedback from students is positive and encouraging.

Locust outbreaks pose persistent challenges for agriculture and food security due to their pronounced spatiotemporal complexity. Existing monitoring and modelling approaches often struggle with sparse and biased field observations, cloud-affected and discontinuous satellite time series, and the difficulty of fusing heterogeneous data across scales. This paper reviews spatiotemporal locust modelling methods under a unified remote sensing–eco-statistical coupling framework. We first summarize multi-source observational inputs, including optical and microwave remote sensing, reanalysis meteorological data, and ground surveys, and outline common workflows for spatial alignment, temporal aggregation, lag handling, and uncertainty-aware quality control. We then examine three major model families along a coherent pathway from behavioural processes to probabilistic inference: Markov and semi-Markov models for explicit state transitions and duration; hidden Markov and state-space models for representing latent ecological states while correcting observation error; and hierarchical Bayesian spatiotemporal models, including INLA-based implementations, for cross-scale integration and formal uncertainty quantification. Building on this synthesis, we propose practice-oriented principles for model selection that account for state observability, temporal structure, spatial dependence, uncertainty representation, data and computational costs, and interpretability. Finally, we discuss a data–model–decision loop that links probabilistic risk products to operational thresholds, surveillance strategies, and control actions. The review aims to support more robust, transparent, and operationally useful early warning and resource allocation for locust management.

Recent increases in extreme climate events and urban accidents highlight the need for urban digital twin technologies capable of real-time monitoring and predictive simulation. However, existing digital twin systems primarily focus on visually realistic three-dimensional representations, which makes large-scale safety simulations computationally expensive due to massive 3D datasets and complex physical models.

To address this limitation, this study proposes a Hash interaction algorithm based on a variable 3D GeoHash framework for generating urban object information and enabling lightweight spatial interaction simulations. The framework extends conventional two-dimensional GeoHash by incorporating elevation to construct hierarchical 3D GeoHash cells that support efficient geocoding of urban objects.

The proposed method consists of four key processes: (1) classification of urban objects into fixed spatial information (e.g., buildings, roads, and terrain) and dynamic spatial information (e.g., weather conditions and moving entities); (2) generation of object-specific attribute information and physical properties; (3) establishment of movement rules between neighboring GeoHash cells; and (4) development of a rule-based inter-Hash interaction algorithm that updates physical state variables through interactions with adjacent cells.

By restricting interaction calculations to neighboring Hash cells, the proposed approach significantly reduces computational complexity while maintaining real-time update capability. The adjustable GeoHash resolution also enables simulations ranging from city-scale environments to centimeter-level spatial detail, supporting lightweight digital twin applications for urban safety management and construction-site monitoring.

This project proposes a GIS-based decision-support concept integrated within the electronic flight bag (EFB) to assist pilots in tactical in-flight weather deviation under convective conditions. The project addresses a critical gap between experience-driven cockpit decision-making, primarily relying on onboard weather radar imagery, and optimisation-based trajectory planning methods that are typically designed for strategic or air traffic management contexts rather than real-time pilot use.

The proposed framework utilises weather radar-aligned data combined with geospatial layers such as terrain, airways, and traffic to construct a unified operational environment. Within this GIS-based architecture, optimisation techniques (e.g., rapidly-exploring random trees, deep reinforcement learning) are applied to generate feasible and hazard-aware deviation trajectories. These trajectories are presented to pilots as advisory “ghost” flypaths on the EFB, supported by quantitative metrics such as weather clearance, additional track distance, and estimated fuel or time penalties, while maintaining the pilot fully in the decision loop.

Expected outcomes include improved flight efficiency through reductions in track mileage, deviation time, fuel consumption, and enhanced safety margins. Furthermore, the system aims to reduce pilot cognitive workload and stress by externalising complex decision-making processes and providing clear, optimised guidance during time-critical situations.

Overall, the project offers a practical cockpit-deployable solution that bridges weather radar-based situational awareness and advanced optimisation methods, enabling more consistent, data-driven, and operationally robust pilot decision-making.

This study proposes a Spatio-Temporal and Semantic Correlation Graph Convolutional Network (STS-GCN) to enhance the prediction of urban emergency events. Addressing the limitation of existing models that fail to fully integrate multi-dimensional correlations, the STS-GCN framework jointly models spatial, temporal, and semantic (categorical) dependencies. The model constructs distinct graphs to represent these relationships, using Graph Convolutional Networks (GCNs) to extract and fuse spatial and semantic features. A Gated Recurrent Unit (GRU) is then employed to capture temporal dynamics.

Trained and validated on a 2015 dataset from the Toronto Police Service—categorizing events into traffic collisions, shootings, robberies, and assaults—the model was evaluated against several baselines. Experimental results demonstrated that the STS-GCN model achieved superior performance, obtaining the lowest RMSE (0.1829) and MAE (0.0023), and the highest Accuracy (0.8705). The study concludes that through effectively learning the complex internal patterns of events through multi-dimensional feature modeling, the proposed framework offers a robust and generalizable tool for accurate urban emergency prediction, with significant potential to support public safety governance and resource allocation.

With the deep integration of geographic information science and human-computer interaction technology, how to support multiple users to cross different physical spaces and collaboratively perceive, analyze, and make decisions on complex geographic phenomena in a unified virtual and real fusion environment has become a cutting-edge challenge in this field. This article proposes a systematic mixed reality collaborative visual analysis method for the collaborative geographic cognition needs across geographic scenarios. The paper first analyzes the core scientific issues of cross geographical scenario collaborative analysis, namely the coupling representation of geographical scenarios and the collaborative aggregation of multi-user cognition. In response to this, we have constructed a four in one theoretical framework of "data model view interaction". The results show that this method can effectively break geographical isolation, build an immersive "co environment" collaborative space, and significantly improve the situational perception ability, communication efficiency, and collaborative decision-making quality of multi domain experts in complex geographical problems. This study not only provides cutting-edge collaborative analysis tools for geographic information science, but also provides important methodological support for interdisciplinary directions such as spatial human-computer interaction and group geographic cognition.

This study analyzes the spatial accessibility and distribution patterns of elderly care facilities in six central urban districts of Beijing (Dongcheng, Xicheng, Haidian, Chaoyang, Fengtai, Shijingshan) against the backdrop of rapid population aging. Using methods such as the Two-Step Floating Catchment Area (2SFCA) and kernel density analysis, the research integrates multi-source spatial and socio-economic data. Results reveal an unbalanced spatial distribution of facilities and varying service capacities, with insufficient coverage within a 5-minute travel scope and improved but transport-dependent accessibility within 10 minutes. The study highlights challenges in achieving “nearby elderly care”, particularly in areas like Fengtai District. It recommends optimizing facility layout by repurposing existing spaces in core areas and constructing new facilities in underserved peripheral zones, in line with “community-based” and “home-based” elderly care principles, to better meet the needs of the aging population.

As technologies such as intelligent connectivity and artificial intelligence become increasingly mature, and the platform economy evolves at a rapid pace, new products, business formats, and models—including autonomous driving, unmanned driving, and the low-altitude economy—are transitioning from pilot demonstrations to application trials, and beginning to enter widespread practical use on a large scale. The advancement of these new technologies, business formats, and models is driving the "ubiquitization" of surveying and mapping. It has become feasible to illegally obtain large-scale, precise location information of ground features quickly in a short period, which poses severe challenges to the supervision and administration of surveying, mapping, and geographic information security. This paper first introduces geographic information data security technologies, including the classification and grading of geographic information data and the confidentiality processing of geographic information data, among others. Secondly, it designs a geographic information security supervision and data governance model, covering geographic information data application scenarios, the geographic information data circulation control model, the geographic information data circulation security architecture and its applications, etc. Finally, it summarizes the challenges and opportunities faced by geographic information security supervision and data governance.

This study investigates the potential of GeoAI and instance-based segmentation for high-resolution land cover classification in San Vito di Cadore (Veneto), a UNESCO mountain region in the Dolomites characterized by high ecological heterogeneity. The dataset comprises 650 manually annotated orthophotos at a spatial resolution of 0.1–0.5 m, labelled across seven main land cover classes (Forest, Shrubland, Grassland, Cropland, Water Bodies, Artificial/Urban Areas, and Rocky/Bare Areas) and harmonized with Corine Land Cover (CLC) aggregated categories for inter-comparison. Snow and cloud were treated as auxiliary classes given their frequent occurrence in alpine imagery. The YOLOv11 instance-segmentation model was trained on 1000×1000 px tiles, with a SAHI (Slicing Aided Hyper Inference) framework adopted during inference to process large-scale orthophotos without loss of spatial quality. Results show an overall precision of 0.847 and recall of 0.575, with mAP@0.5 exceeding 0.65. Quantitative comparison with the Regione Veneto land cover product (2023) reveals good agreement for the dominant forest class (−1.4%), while the largest discrepancies concern artificial surfaces (+20.8%) and agricultural areas (−36.3%), attributed to differences in spatial scale and training-sample imbalance. The work highlights the advantages of instance-aware deep learning for generating accurate, spatially coherent land cover maps and underlines the growing relevance of GeoAI workflows for environmental monitoring and spatial planning in complex mountain environments.

Urban building clusters can significantly alter the low-altitude urban wind field, creating local regions with high wind speed, intense turbulence, and strong non-uniform disturbances. These conditions may cause lateral deviation, attitude instability, increased trajectory error, and even exceed the wind-resistance limits of low-altitude UAVs. To address the lack of explicit modeling of local high-risk wind regions in traditional path planning methods, this paper proposes a wind-risk-aware path planning method, termed RP- A* (Wind Risk-Aware Probability A*). First, a steady-state RANS urban airflow model with the standard k-ε turbulence model is used to obtain the mean wind field and turbulence statistics in the low-altitude flight region. Second, the CFD results are reconstructed on a grid to build a wind risk model consisting of gust risk, crosswind risk, and overall wind-limit-exceedance risk. Third, a direction-dependent integrated risk cost is introduced into the A* search framework, and the RP-A* algorithm is developed to achieve path planning that balances route efficiency and flight safety. Finally, Monte Carlo simulations driven by turbulence-based wind perturbation samples are conducted to estimate the empirical failure rate of planned paths. Results show that RP-A* significantly reduces path failure risk compared with baseline shortest-path methods while requiring only a limited increase in path length. The proposed framework provides an effective approach for safe UAV path planning in complex urban low-altitude environments.

The sustainable development of historic districts, aligned with UN SDG 11.4, requires integrated approaches that balance heritage preservation with contemporary urban functionality. This study proposes a novel analytical framework combining Space Syntax theory and Point of Interest (POI) data to address this challenge. Departing from traditional non-hierarchical methods, approach of the study innovatively processes vectorized road networks with a focus on community and block-scale hierarchy, more accurately reflecting human-scale spatial perception and connectivity. This refined Space Syntax model quantitatively analyzes street accessibility and spatial configuration, which is then integrated with categorized POI data to reveal the inherent socio-economic logic and functional distribution within historic urban areas. The framework is empirically validated through a case study of a historic district in Kunming, China. Results demonstrate that this combined methodology provides a comprehensive understanding of the spatial organization, offering data-driven insights to support precise and sustainable conservation and renewal strategies for historic districts.

This study addresses the challenge of automatically recognizing and extracting spatial coordinates from unstructured natural language texts, particularly within the domain of land registry documents (fiduciary documents). It proposes a deep learning-based method utilizing the BERTimbau language model, fine-tuned for the Named Entity Recognition (NER) task. This research expands the scope of geoparsing beyond the traditional focus on toponyms, specifically targeting the direct extraction of coordinate data for reliable automation in engineering, land cadastre, and land regularization.

Urban densification in post-socialist cities involves fine-scale spatial transformations that are difficult to quantify in data-scarce environments.

This study proposes FLAIR-HUB2BF, a model inference-based workflow for automated building footprint extraction

and multi-temporal change analysis, applied to the city of Iasi, Romania. The methodology extends the SUBDENSE conceptual

framework by integrating the FLAIR-HUB deep learning model for semantic segmentation of very high resolution aerial orthophotos

from 2011 and 2024, followed by binary mask extraction, instance segmentation, and Douglas–Peucker polygon generalization,

yielding 34,454 and 17,141 georeferenced building footprints, respectively. The approach demonstrates that coherent building

footprint datasets and their temporal evolution can be derived directly from aerial imagery without relying on complete cadastral

databases. To support rigorous evaluation, the first open benchmark building footprint datasets for Romania were produced through

manual photo-interpretation correction across four morphologically distinct urban neighborhoods of Iasi, and assessed against ISO standards

19157 spatial data quality standards, achieving commission and omission rates of 1.95% and 2.39%, respectively. Quantitative

evaluation using complementary GMA (Geometric Matching of areas) and MCA (Multi-Criteria Algorithm) data matching algorithms

confirms moderate-to-high spatial accuracy, with MCA surface agreement rates reaching 91%. The results demonstrate

the capability of the method to capture fine-scale urban transformations, including infill development, brownfield redevelopment,

and peri-urban expansion, while revealing the critical influence of input data quality on segmentation performance. The proposed

workflow establishes a transferable, reproducible, and open methodology for building-level urban monitoring applicable to other

Romanian and European cities facing similar data constraints.

Augmented Reality (AR) offers an immersive medium for visualizing 3D city models directly within physical environments, but current systems lack real-time synchronization with authoritative geospatial databases. This paper suggests an open-source architecture that bridges this gap by enabling bidirectional, standards-compliant communication between AR Microsoft HoloLens 2 frontend and a CityGML-based backend. The system integrates PostgreSQL/PostGIS with 3DCityDB, exposed through a Django API, and connects to AR front-ends such as Microsoft HoloLens 2 via Cesium for Unreal.

This work proposes an extension to the OpenDRIVE standard to represent pavement surface defects, improving the realism of autonomous vehicle simulations and enabling the integration of road condition data from modern mapping and AI-based detection methods.

In cultural heritage research, one task is to reconstruct historical ship routes; however often exact trajectories are lacking, and thus an accurate itinerary is difficult to reconstruct due to inaccurate or incomplete documentation. The aim of this work is therefore to create spaces of uncertainty as so-called possibility spaces based on the calculation of a geographical extent that attempts to encompass all valid path options.

In order to obtain meaningful possibility spaces, it is important to first define the navigable space and also take into account additional factors such as water depths, currents, wind direction and known historical shipping lanes that may influence a possible route. This information can be used to define the cost for calculating possible routes.

To calculate possibility spaces, different approaches of path planning are explored, such as transferring the navigable space into a routing graph, converting the space into a regular grid, or using an irregular grid in terms of a mesh.

Subsequently, options for deriving the final possibility spaces are described, such as using the explored nodes during the search process (e.g. using A*), or to generate a possibility space by creating a variation of paths by calculating k-shortest paths. Of particular interest is the calculation of paths that have a cost value similar to a predefined acceptable maximum. These paths form the outer boundaries of the possibility space to be created.

High-definition (HD) maps are a key digital infrastructure for connected and autonomous vehicles, especially in highway environments where detailed and reliable road representations are required. This contribution presents an end-to-end workflow for HD road map generation from mobile mapping data, developed within the HD SMART MAP project (PNRR Spoke 7) and applied to a 10 km stretch of the Tangenziale di Napoli.

The survey was carried out with the GAIA M1 Mobile Mapping System, integrating LiDAR, panoramic imagery and GNSS/INS. This configuration enabled the acquisition of dense point clouds and synchronized images even in GNSS-challenging areas such as tunnels. All data were georeferenced in the national reference system RDN2008, with heights referred to the ITALGEO2005 geoid.

The processing pipeline includes point cloud filtering, ground segmentation and DTM generation, as well as the production of an orthophoto of the corridor from panoramic imagery. These products support the semi-automatic extraction of lane markings and road features, which are then encoded according to the ASAM OpenDRIVE standard.

The resulting HD map provides a geometrically and semantically rich, machine-readable description of the highway, suitable for vehicle localization, path planning and simulation. The case study demonstrates that semi-automated procedures significantly speed up HD map production compared to traditional manual workflows.

Vector geometries are traditionally represented as entities of sequences of coordinate structures. With advancements in hardware and software for data analytics, a preference for columnar data layouts arose. This paper examines array programming for spatial operations to evaluate the potential performance benefits of modern computing architectures and emerging spatial data encodings. Evaluating selected operations, such as distance calculation, extent extraction, and affine transformations, indicates similar or improved performance for geometries with columnar coordinate layouts. By leveraging modern compiler infrastructure, we further demonstrate that advanced hardware features in commodity hardware, such as vector instructions, are becoming accessible without specialized code. The performance comparison with established, widely used geospatial and computational libraries reveals significant untapped potential for increasing the efficiency of spatial computing.

Recent advances in geomatics and 3D surveying have enabled the acquisition of increasingly dense point clouds through both static and mobile laser scanning systems, supporting the rapid digital documentation of built and natural environments. At the same time, the growing diffusion of WebGL technologies has opened new possibilities for the online visualization and dissemination of complex three-dimensional datasets. Within this context, the present study proposes an open-source workflow for the automatic extraction of significant geometric surfaces from laser scanner point clouds and their integration into a web-based visualization framework. The method was developed within a Python-based processing environment and tested on three datasets characterized by different levels of geometric complexity: a regular built environment, an under-construction building environment, and a historical context. The workflow includes point cloud preprocessing, automated segmentation strategies adapted to the geometric complexity of each case, extraction of planar and non-planar elements, polygonal surface generation, mesh construction, and conversion of outputs into lightweight formats suitable for web publication. The final visualization environment combines segmented polygonal models and subsampled point cloud data through open-source WebGL technologies. The results demonstrate that the proposed strategy provides a scalable and flexible solution for the rapid online representation of large laser scanner datasets, supporting surface recognition, low-cost accessibility, and future semantic enrichment within web-based geospatial and Digital Twin applications.

This paper reports progress in the Venaria Reale pilot of the EU Horizon Europe project HERITALISE (2025–2028), using the Galleria Grande as a test case for a preventive-conservation workflow toward digital twin integration. It presents a reproducible Scan-to-BIM workflow for HBIM that delivers a 3D backbone combining geometric reliability, semantic queryability, and source traceability. Multi-sensor datasets, including terrestrial laser scanning, SLAM-based mobile mapping, and UAV photogrammetry, are georeferenced within a unified coordinate framework. A georeferenced UAV–TLS fused point cloud serves as the main modelling baseline, while SLAM data are used only as local infill for verified missing areas. Data management follows a raw-working-deliverable structure with logged parameters, transformation matrices, and quality-control notes. Registration residuals are controlled within 0.01–0.05 m and checked through section-based and distance-based validation in critical junction areas. Geometric modelling adopts a Revit-Rhino workflow guided by structural, semantic, evidential, and feasibility criteria. Semantic enrichment follows the HERITALISE Common Data Environment and BIM Execution Plan, with ObjectID linking HBIM elements to an external SQL database and supporting continuity between legacy and current room naming systems.

Landslide susceptibility assessments have traditionally used static rainfall statistics that do not reflect the actual meteorological conditions when slopes fail. This study develops a machine learning framework that aligns high-resolution radar rainfall (XRAIN, 250 m / 10 min) and modeled soil moisture (XSWI) with documented landslide occurrence times as dynamic triggering factors. Applied to the Heavy Rain Event of July 2018 in Hiroshima Prefecture, the framework combines watershed-based spatial cross-validation, systematic comparison of four class imbalance strategies (no treatment, sample weighting, random under-sampling, and SMOTE) across eight algorithms (XGBoost, LightGBM, CatBoost, HGBoost, Random Forest, Balanced Random Forest, Easy Ensemble Classifier, and Logistic Regression), and spatially explicit SHAP interpretation.

Two key findings emerged. First, soil moisture state — not instantaneous rainfall intensity — was the dominant triggering predictor: XSWI variables ranked 2nd and 3rd in importance after slope angle, operating as independent axes (r = 0.074). The no-treatment condition consistently outperformed all resampling strategies across algorithms. Second, spatial SHAP mapping revealed that predisposing factors produce time-invariant contribution patterns governed by terrain, while dynamic triggers produce event-specific patterns reflecting rainfall distribution; their spatial overlap identifies the highest-risk locations. Time-series susceptibility maps confirmed that the framework captures within-event risk evolution as rainfall progresses — a capability unattainable with static approaches. These results indicate that incorporating occurrence-time-aligned soil moisture dynamics substantially improves both the predictive and explanatory capacity of landslide susceptibility assessment.

Accurate air quality monitoring depends on continuous satellite observations of key pollutants such as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) from the Sentinel-5P/TROPOMI mission. However, these datasets often suffer from severe spatio-temporal discontinuities due to cloud cover, surface reflectance, viewing geometry, and strict quality filtering, which limit their reliability for environmental and health-related applications.

This study addresses the challenge of missing data reconstruction over the Po Valley, Northern Italy (2019-2023), an area characterized by complex terrain and frequent winter inversions that amplify data gaps. A comprehensive statistical analysis revealed substantial data loss, averaging 45% for NO₂ and 77% for SO₂, with pronounced seasonality and strong correlations with elevation.

To fill these gaps, an integrated machine learning framework was developed, combining a LightGBM baseline model and a 3D Convolutional Neural Network (3D CNN). The models exploit multi-source predictors, including meteorological variables (ERA5), atmospheric priors (CAMS), topography, land cover, and population density, together with cyclic temporal encoding.

Preliminary results demonstrate that the 3D CNN significantly improves gap reconstruction performance (R² = 0.95 for NO₂, 0.74 for SO₂) compared to the LightGBM baseline, though at higher computational cost. The proposed framework enhances the spatio-temporal continuity and usability of Sentinel-5P data, supporting more reliable environmental monitoring and policy-making in data-sparse conditions. Future work will extend the approach to other pollutants, regions, and deep learning architectures.

The Ecuadorian Amazon hosts remarkable biodiversity and cultural richness but faces increasing pressures from the expansion of oil-related activities. This study evaluated the distribution and concentration of deforestation hotspots between 2000 and 2023, analyzing their relationship with existing oil infrastructure and environmentally significant areasLand Use and Land Cover data from MapBiomas Ecuador were combined with Kernel Density Estimation (KDE) analyses based on the spatial distribution of oil blocks, pipelines, wells, and the limits of environmentally sensitive areas. The results indicate a net loss of 391,303 ha (4%) of forest cover, with 80% of the hotspots located within a 10 km radius of hydrocarbon infrastructure. However, intangible zones, protected areas, and water protection zones showed minimal impacts. The findings of this study provide technical evidence to support land-use management and conservation efforts in ecologically vulnerable Amazonian regions.

Nighttime light (NTL) Earth observation data represent an invaluable resource for measuring population distribution, disaster impact, economic activity, and socio-economic inequalities from space. While traditional NTL data sources provide consistent long-term measurements, they are spatially coarse, impeding spatially detailed analyses of nighttime lights. Novel, high-resolution NTL data from the SDGSAT-1 satellite capture NTL variations across space and time at fine spatial detail of 10 to 40 m and open new research avenues but also require innovative analytical approaches. Herein, we demonstrate the potential of jointly assessing annual SDGSAT-1 composites and human settlement data from the Global Human Settlement Layer (GHSL) and other data, characterizing the built environment, human population distribution, and the rural-urban continuum. We illustrate that such data integration generates new insights on the interactions of nighttime lights and settlement-related characteristics at unprecedented detail. For example, we find that NTL emissions tend to be highest in old parts of settlements (<1975) and lowest in very recently developed land. The brightness of major roads and non-residential areas at night approximately doubles, on average, compared to residential built-up areas. ~80% of urban population resides in areas characterized by luminous, stationary NTL, while this population share drops to ~15% in very rural areas. Looking at infrastructure-related land use, we find that airports emit the highest levels of stationary and non-stationary NTL in our study area. These results illustrate the potential of high-resolution data from SDGSAT-1 and pave the way forward to include such data in settlement and population modelling at scale.

The POSEIDON project aims to develop scalable and repeatable approaches for monitoring Posidonia Oceanica (PO) meadows, a key Mediterranean habitat that supports coastal ecosystem services and long-term blue-carbon storage, yet they are increasingly threatened by warming waters and cumulative human pressures. This work presents a satellite-based workflow for benthic habitat mapping that combines Sentinel-2 multispectral imagery, ancillary bathymetry, and deep-learning semantic segmentation. Sentinel-2 Level-2A data and bathymetry were integrated into multi-band inputs on a common 10 m grid, with analysis restricted to water pixels. A wall-to-wall reference map was generated by harmonising existing habitat products into six benthic classes for supervised model training and evaluation. U-Net and DeepLabv3 architectures with a ResNet backbone were tested for a representative September 2015 scene. The workflow was first assessed in the Culuccia peninsula, where it achieved an overall accuracy of 0.830 and a Kappa coefficient of 0.786. It was then successfully transferred to the Capo Testa - Punta Falcone Marine Protected Area (MPA), where the best-performing configuration reached an overall accuracy of 0.882 and a Kappa coefficient of 0.843. These results show that open-access satellite data combined with robust semantic segmentation models can provide a reliable and non-destructive framework for seagrass mapping in complex coastal environments.

Nowadays, with technological advancements and the increasing volume of urban traffic, low-altitude urban air mobility, particularly for time-sensitive trips such as airport travel, has emerged as a promising solution. Vertiports are one of the key components of this novel transportation system, serving as the ground connection points for urban air mobility operations. The optimal placement of vertiports, considering various influencing factors, is critical to the successful implementation of this emerging mode of transportation.

In this study, a data-driven framework is proposed to identify the most suitable areas for vertiport placement in order to facilitate and accelerate access to airports in the City of Toronto. By integrating environmental constraints, population density, ground transportation connectivity, noise impact zones, and regulatory considerations, the framework evaluates land suitability using GIS-based analysis and a deep-learning approach called Generative Adversarial Network (GAN). The proposed methodology can generate a vertiport network by learning nonlinear spatial relationships between multiple spatial layers, without the need for subjective rules, and finally identifying potential vertiport locations with maximum coverage. The results demonstrate two strategically located vertiports for accessing each of Billy Bishop and Pearson airports, situated in commercial, mixed-use, and industrial zones, high-demand areas, and locations near major public transit stations.

In this paper, we investigate the use of CityGML data for visual localization. Therefore, we present a visual localization approach that uses CityGML data. We compare different matching approaches for real images and renderings of CityGML data and evaluate our results using query images with accurate ground truth poses. We show that pose estimation is possible with object features of city models. We propose an evaluation of the estimated pose with independent ground truth poses from the reference data.

Achieving a genuine digital twin for smart buildings requires accurate and real-time knowledge of the spatial positions of users and objects within indoor environments. Despite major advances in Indoor Positioning Systems (IPS), most existing frameworks lack a structured data connection with Building Information Modeling (BIM) which provides the semantic and geometric representation of building elements and spaces. This disconnects limits both real-time synchronization and three-dimensional spatial visualization. This study presents a novel BIM-driven hybrid framework that integrates Wi-Fi and Bluetooth Low Energy (BLE) signal data with BIM to establish a data-centric foundation for digital-twin development. The experimental setup was deployed on the fourth floor of the Faculty of Geography at the University of Tehran, modeled as a BIM-based indoor test environment. Received Signal Strength (RSS) data were collected from 35 reference points (RPs) and seven transmitters (four Wi-Fi access points and three BLE beacons), normalized, and processed using both Fingerprinting and Trilateration models. By incorporating the vertical component (Z) and linking spatial records to BIM entities such as IfcStorey and IfcSpace, user locations were visualized within a 3D building model. The BLE-enhanced Fingerprinting model achieved a substantially lower error (RMSE ≈ 0.40 m) than Trilateration (RMSE = 2.38 m), while the final hybrid model, employing adaptive weighting between sub-models, achieved more than 95 % accuracy within one meter of the ground truth.

These results demonstrate that integrating IPS data with BIM provides a robust foundation for digital-twin creation in smart buildings.

Bottom-up, physics-based Urbain Building Energy Modelling (UBEM) approaches enable systematic assessment of building typologies and operational behaviours even when empirical data are limited, providing robust results for district-scale heating and cooling simulations. However, most physics-based UBEM applications have focused chiefly on building-related parameters and have given limited attention to environmental factors such as vegetation, although this element affects building energy demand under changing climate conditions.

To overcome this limitation, the paper will present a simulation-based workflow that evaluates how urban tree-planting strategies influence building heating and cooling demand under current and projected 2050 climate conditions. Specifically, the workflow builds upon the simulation-based UBEM platform SimStadt by embedding vegetation effects directly within a single modelling environment, removing the need for external microclimate coupling or additional simulation tools. Our method is based on standardised CityGML building models, simplified yet seasonally dynamic vegetation representations, and a unified modelling environment that allows consistent comparison of cooling and heating demand under both current and projected climate conditions. This integration allows for the quantitative evaluation of tree-planting strategies for both heating and cooling demand at the district scale.

The paper will present the results of the study carried out in some neighbours of the Dutch city of Rotterdam.

Cities are at the core of the current debate on climate change mitigation, and multiple policies on the global and continental scale have acknowledged this condition, pushing towards an increase in the renovation rate and the installation of renewable energy technologies. The revised Energy Performance of Buildings Directive (European Parliament and Council, 2024) targets the renovation of 35 million buildings across Europe, starting from worst-performing buildings. The authoritative tool for the identification of such buildings is the Energy Performance Certificate, the European reference scheme for energy performance in buildings, which covers only a fraction of the European building stock, approximately 30-50%. This study aims to refine a published methodology which takes advantage of aerial thermography and Energy Performance Certificates to attribute an energy class to the whole building stock. The research question is how to classify the building stock, thus making it possible to compute the final energy consumption, by adopting a geospatial approach which considers simultaneously remotely-sensed and metered data. This research considers three main inputs: a thermal point cloud, the building layer of the technical map of the City of Turin, and Energy Performance Certificates.The method is based on the assumption that all buildings have the same indoor temperature. For this reason, the external surface temperature is a proxy of the quality of the envelope, with low-performing buildings having higher thermal losses and therefore an higher external temperature. Then, the class distribution is observed in the Energy Performance Certificates database and replicated in the whole building stock.

Time series are an important source of information about changes in land cover. However, historical raster datasets are often characterized by vague and imprecise temporal properties. We have developed a novel raster data management system designed specifically for machine-learning applications, which organizes temporally vague raster data in cloud environments. The system addresses the challenges of processing historical maps with uncertain temporal attributes. It combines object storage, PostGIS Raster and the Spatio-Temporal Asset Catalogue (STAC) API, enabling the efficient, interoperable management of spatio-temporal raster data. It allows users to define and evaluate vague instants and fuzzy intervals, enabling them to perform precise queries on temporally relevant datasets. This solution is particularly useful for managing databases in a flexible and customizable way, and is ideal for sovereign data management and self-managed infrastructures.

This study investigates the long-term evolution of Kenya’s road network from 1950 to 2020, highlighting how colonial legacies, post-independence modernization, and contemporary planning have shaped infrastructure development. Using deep learning techniques, roads were extracted from historical topographic maps (1950–1980) and transformed into GIS-compatible data, resulting in a nationwide road dataset of approximately 56,000 km from the mid-20th century. These data were compared with a 2020 dataset from the Kenya Roads Board, which documents over 161,000 km of roads. The analysis reveals that Kenya’s total road length has nearly tripled, and the average distance to the nearest road has decreased from 8.6 km to 5.1 km. However, the road development is uneven across the country: southern and central regions show significant growth, while northern and arid areas remain underserved, reflecting persistent spatial disparities rooted in colonial planning. A regional comparison in southwestern Kenya shows a 56% increase in road length between the 1950s/60s and 1970s/80s, with notable upgrades in road quality. The proportion of paved roads rose from 1.5% to 12%, and tertiary dry-weather roads declined from 64% to 26%. Despite these improvements, only 15–30% of Kenya’s roads are paved today, which is below the continental average of 47%. This study demonstrates the value of integrating historical and contemporary geospatial data to assess infrastructure development, identify gaps, and support planning aligned with Kenya Vision 2030 and the Sustainable Development Goals. The findings underscore the importance of spatial analysis in evaluating development outcomes and guiding future investment strategies.

Knowledge of the regional distribution of the world’s population is essential for political and social decisions not only but especially for achieving the 17 Sustainable Development Goals (SDGs). Census and other population data at the subnational level are important for this purpose. However, current population data management platforms largely ignore the spatiotemporal nature of census data. Here, we outline the requirements for a spatiotemporal population data management system and present its general architecture, data model and state of implementation. The developed system currently stores population data from approximately 200 countries, nearly 11 million spatial units and around 770 million individual population figures.

Managing dynamic geographic knowledge effectively is hindered by fragmented tools lacking holistic integration, particularly when handling the heterogeneous and evolving nature of real world spatio-temporal data. Traditional knowledge graphs and databases struggle with efficient representation, storage, and reasoning over such complex information. This paper propose an integrated computation framework built upon Grid Graph Modelling to make geography computable. This framework provides an end-to-end solution encompassing knowledge representation, storage, querying, and spatio-temporal reasoning. It synergistically integrates three core components: the Grid Augmented Geographic Knowledge Graph (AugGKG) for unified grid based representation with computable spatial relations; the Grid Graph Database (GGD) for spatially aware storage and efficient grid algebra based computation; and the Grid Neighborhood-based Graph Convolutional Network (GN-GCN) for advanced reasoning by learning from semantic, spatial grid, and temporal dimensions. This cohesive architecture transforms diverse geographic data into actionable knowledge, enabling efficient querying and complex reasoning, paving the way for next generation intelligent geospatial systems, including empowering foundation models, enhancing smart cities, creating digital twins, and reasoning geographic event evolution.

This paper introduces a method to analyze the effect of aggregation on continuous (interval or ratio scale) raster data. Previous research used the entropy based local indicator of spatial association (ELSA) to study the change in the local spatial association this, new paper extends that idea by evaluating both the within and between pixel variability. The standard deviation was used to evaluate the between pixel variability with a decrease in the SD indicating a decrease in the image information content. Ec (diversity) is one part of the ELSA statistic and gives a measure of the within-pixel heterogeneity. We should balance the this within and between-pixel variability when choosing the pixel size for a raster dataset. The variogram was used to explore the change in spatial structure. Current research is refining this method and developing a tool that will support the user to choose the pixel size for mapping.

Current research is following two further avenues. The first is to adapt this method for categorical data with an application in land cover mapping. Second is to build in the effect of predictive uncertainty in the pixel values.

Carrier phase observations enable millimeter-level GNSS positioning, but their continuity is frequently disrupted by signal blockages and cycle slips. This limitation is particularly critical for low-cost and smartphone receivers, where weak antennas, urban multipath, and duty cycling cause frequent phase gaps that prevent reliable ambiguity resolution. Before addressing the full complexity of mass-market observations, the prediction methodology must be validated under controlled conditions. In this work we investigate whether machine learning, supported by precise satellite orbits and clocks, can predict carrier phase observations during signal gaps with millimeter-level accuracy. Twenty-four hours of Galileo data from the TORI permanent station (SPIN3 network, Torino, Italy) are processed at 30~s sampling using GFZ final SP3 and CLK products. After forming the ionosphere-free combination, an iterative carrier-phase based estimator removes the receiver clock, tropospheric delay, and ambiguity, reducing the residuals to a median standard deviation of 60~mm. Synthetic gaps from 60~s to 1800~s are introduced (1045 gaps total) and four prediction strategies are compared: polynomial fitting (degrees~3 and~5), Fourier-augmented polynomial, Gradient Boosting Regression with satellite geometry features, and Gaussian Process Regression. The Gradient Boosting model achieves the best overall performance, reaching 4.4~mm RMS for 60~s gaps, 9.4~mm for 5~min gaps, and 21~mm for 30~min gaps, well below the half-wavelength threshold required for cycle slip repair. These results demonstrate that geometry-aware gap prediction is feasible at the sub-wavelength level, providing a validated foundation for extending the approach to low-cost and smartphone GNSS receivers.

Indoor maps are essential for robot services, but the high dynamics of indoor environments caused by human activities lead to frequent layout changes, making it challenging to maintain map accuracy and timeliness. Existing map update methods, such as periodic full reconstruction or event-triggered incremental updates (Prieto-Fernández et al., 2024; Xia et al., 2024), lack a quantitative mechanism to evaluate whether element layouts are sensible. This makes it difficult to predict systematic changes and creates a paradox between "update frequency and element granularity." To overcome these limitations, this study proposes a spatial cognition-driven approach to identify the rationality of indoor element layouts, providing a predictive metric for efficient, layered map updates and enabling advanced robot navigation and safety warnings.

To address limited high-level semantic modeling in indoor 3D point cloud segmentation, this study proposes a text-knowledge-guided framework built upon RandLA-Net. Category-level textual semantic prototypes are constructed through multi-template prompting and encoded by a pre-trained text encoder to provide stable semantic priors. These textual cues are progressively integrated into point cloud feature learning through shallow semantic modulation and high-level cross-modal fusion, enhancing the interaction between geometric representations and semantic knowledge. The network is jointly optimized by segmentation supervision, prototype alignment, and boundary refinement, enabling it to learn discriminative features that preserve local geometric details while encoding richer semantics.

Superpoint matching is a critical step in coarse-to-fine point cloud registration, and its performance directly affects the accuracy of sub-sequent point matching and pose estimation. However, most existing methods establish correspondences mainly relying on feature sim-ilarity, without explicit modeling of spatial structure, which easily leads to unstable matching in complex scenarios such as noise, occlu-sion, and low overlap. To address these issues, this paper proposes a coarse-to-fine point cloud registration method guided by prior correspondences. First, prior superpoint correspondences are constructed using rigid transformations estimated by existing SOTA methods, and are serially encoded via a prior encoding module to provide explicit constraints for feature learning. Furthermore, multiple geometric information including pairwise distances, angles, and normals is introduced and uniformly encoded to enhance spatial struc-ture representation. On this basis, a prior-guided sparse mixture-of-experts attention mechanism is designed to differentially model fea-tures in overlapping and non-overlapping regions, thereby improving feature discriminability and structural consistency. Using the learned features, the model gradually establishes correspondences through superpoint matching and point matching, and estimates the final rigid transformation with RANSAC. Experiments on the 3DMatch dataset show that when sampling 1000 point correspondences, the proposed method achieves an inlier ratio of 80.7% and a registration recall of 92.9%, which are 5.5% and 1.1% higher than the base-line method respectively, verifying the effectiveness of the proposed method in terms of accuracy and robustness.

Climate change is reshaping the global water cycle, causing substantial alterations in precipitation, evaporation, and runoff patterns. These shifts are driving rapid changes in terrestrial water storage (TWS), which includes groundwater, soil moisture, surface water, snow, and ice. Declining TWS threatens freshwater security, increases the vulnerability of ecosystems and communities, and directly impacts sustainable water management—key concerns addressed under SDG 6 . In parallel, intensifying climate-driven water losses align with the global challenges highlighted in SDG 13, particularly regarding adaptation and resilience.

This study examines long-term TWS variations in the Susurluk Basin of Türkiye’s Marmara Region using NASA’s GRACE and GRACE-FO satellite missions. By measuring gravity anomalies caused by mass changes, GRACE enables the detection of large-scale water storage shifts. Monthly data from 41 GRACE grid points (2002–2022) were processed using the Mann-Kendall trend test at a 5% significance level. Consistent acceptance of the H1 hypothesis and universally negative Z values confirm a statistically significant and persistent decline in TWS across the basin.

Results show that water storage loss accelerated dramatically between 2012 and 2022 compared to 2002–2012. The basin exhibits an overall decreasing coefficient of –0.0561, while sub-basin analyses indicate 20-year average losses ranging from –1.3 cm to –0.1 cm. These findings demonstrate a clear, worsening depletion of water resources, emphasizing the urgent need for climate-adaptive water management. The documented TWS decline underscores the relevance of this work to SDG 6 by highlighting risks to water availability and to SDG 13 through evidence of climate-induced hydrological change.

The aim of this paper is to design and develop an openly accessible, web-based Crowdsourced Geographic Information (CGI) framework, referred to as the Digital Detectives of Environment (DiDE), to facilitate the collection of geo-located events. The framework incorporates three user roles: (i) citizens, (ii) experts, and (iii) supervisors. Citizens can browse relevant events without requiring authentication, while experts are responsible for collecting geographic data, including the optional attachment of photographs or videos. Supervisors, on the other hand, define and manage event types. Each event type is classified as either useful or harmful, which determines its visibility to citizens. The pilot implementation was conducted at the Beytepe Campus of Hacettepe University, focusing on four event types aligned with Green Deal actions: rubbish bins and recycling bins (useful), and cigarette butts and full rubbish/recycling bins (harmful). During a one-week data collection period, a total of 490 events were recorded by 37 students. The results reveal clear clustering patterns in both space and time. Temporally, a large proportion of the data was collected on the final day, indicating a tendency toward procrastination among participants. Spatially, events are concentrated in the southern part of the campus, where most facilities are located. This pattern is further supported by analyses using the F and G functions. In particular, cigarette butt events exhibit strong spatial clustering, with a mean nearest-neighbour distance of approximately 25 metres. This finding provides empirical support for the broken windows theory.

Pavement Management Systems (PMS) are essential for evaluating and maintaining transportation infrastructure; however, conventional monitoring methods are often labour-intensive, costly, and inaccurate. The growing need for reliable. timely pavement condition data has driven the development of automated, data-driven approaches. This study presents a low-cost and scalable framework for pavement condition monitoring that integrates multimodal sensing with a digital twin (DT) environment. Smartphones equipped with inertial measurement unit (IMU) sensors, GPS, and cameras are used to collect synchronized vibration and visual data during normal driving conditions. Vibration signals are analysed to detect anomalies associated with pavement surface irregularities, while video data are processed using a deep learning-based object detection model to identify surface distress. A late fusion approach combines the outputs from both modalities to improve detection reliability and provide comprehensive condition assessment. The system enables spatial mapping of detected distresses and supports real-time visualization through a web-based DT dashboard. Results demonstrate that multimodal sensing compensates for the limitations of individual sensors, enhancing both detection accuracy and robustness. The proposed framework offers a practical solution for efficient pavement monitoring. It supports data-driven decision-making for proactive infrastructure management, with potential for future expansion through crowdsourced data and additional sensing technologies.

To address the challenges of low efficiency and omission in manual inspection of small road debris, this study develops a lightweight mobile monitoring system for fine road debris. The system integrates a high-resolution industrial camera and a GNSS positioning unit to achieve real-time image acquisition and spatial synchronization. Built on the Python platform, the software includes data acquisition and communication modules that enable automatic uploading of images and system status information. To tackle the issues of small-object detection and limited edge-device computing power, an improved Dynamic-YOLOv8n model is proposed by introducing dynamic convolution and attention mechanisms to enhance recognition accuracy for small debris. Field experiments show that the system operates stably at vehicle speeds of 40–70 km/h, achieving an average detection accuracy of 93.2%. The results demonstrate that the proposed system achieves lightweight, real-time, and high-precision detection performance, providing an efficient and practical solution for road safety monitoring and digital maintenance.

Urban Digital Twins (UDT) require systematic integration of heterogeneous 3D geospatial data sources, but existing integration

methods struggle with semantic information loss during fusion, geometric precision degradation through format conversions, and

limited storage scalability. This paper presents a modular, database-centric framework achieving bidirectional semantic enrichment

through semantically enriched voxelization. The framework integrates CityGML building models, Mobile Mapping System (MMS)

point clouds, and Digital Terrain Models (DTM) using PostgreSQL/PostGIS database system with pgPointCloud, the point cloud

extension of Postgres for patch-based storage. A two-stage refinement pipeline is applied to align MMS point clouds to CityGML

wall surfaces using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) algorithms. To integrate the terrain,

Constrained Delaunay Triangulation (CDT) algorithm is applied with building footprints as constraints. All datasets are independ-

ently voxelized at a common configurable resolution, with voxels enriched via custom pgPointCloud schemas storing multi-source

attributes. A unified voxel table merges layers using priority-based conflict resolution. The framework is evaluated in terms of com-

putational performance, registration precision, and storage efficiency, demonstrating feasibility and correctness of the integration

pipeline on a representative urban test case. This paper presents a proof-of-concept evaluated on a small urban area in Hannover,

Germany, demonstrating the framework’s potential for further development.

The increasing frequency and magnitude of landslides necessitates a fundamental shift from reactive mitigation to proactive, predictive risk governance. To define the necessary tools for this transition, this study conducts a systematic literature review and operational analysis of current Digital Twin (DT) implementations in the geosciences. Through this review, we identify four primary target user groups (emergency responders, technical experts, public administrators, and citizens) and map their specific 4D data requirements and interaction logics. Our findings highlight that most existing systems function as "Digital Shadows" characterised by unidirectional data flows and a topography gap, where dynamic sensor data is superimposed onto static, outdated 3D meshes. Based on these requirements, we propose a theoretical layered architectural framework for a Data Hub designed to bridge these gaps. The conceptual architecture is structured into three interconnected tiers: an Acquisition Layer for multi-scale data ingestion; a Modelling and Processing Layer for AI and physics-based stability assessment; and an Application and Service Layer for translating complex data into actionable intelligence. Finally, this work investigates a possible implementation path for landslides DT projects by outlining technical recommendations. This includes the adoption of cloud-native formats (e.g., Cloud Optimized GeoTIFF, Zarr) and unified interoperability standards (e.g., OGC SensorThings API) to evaluate the feasibility of transitioning towards a true bi-directional cyber-physical system for landslide risk management.

Highly Pathogenic Avian Influenza (HPAI), particularly the H5N1 strain, poses a significant ongoing threat to animal health, biodiversity and food security across Europe. Understanding where and when avian influenza risks intensify is essential for targeted surveillance and rapid response. This study develops a data-driven spatio-temporal framework that integrates geospatial, ecological and climatic datasets to explain and forecast the dynamics of H5N1 outbreaks between 2021 and 2024. Weekly country-level outbreak counts (208 weeks, 37 countries) were analysed using a hierarchical endemic-epidemic model with an assumption of Negative Binomial distribution. Environmental covariates, bird-species densities, and human population metrics were incorporated into endemic and autoregressive components. Model performance was evaluated using rolling one-step-ahead forecasts assessed by proper scoring rules (logarithmic score and ranked probability score) and calibration diagnostics. The proposed framework substantially outperformed a regression-only Negative Binomial baseline, reducing mean logS by approximately 29% and RPS by 49%, while exhibiting improved probabilistic calibration. Results indicate that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects. Anseriformes, Charadriiformes and Pelecaniformes densities were identified as the key migratory bird families contributing to the viral spread. The endemic-epidemic model achieved high forecast accuracy, with majority of the of observed weekly outbreak counts falling within central predictive intervals (RPS = 0.76, logS = 0.61). Overall, the proposed framework provides a scalable approach for integrating ecological and spatial information into early-warning systems for HPAI surveillance.

Reliable communication between satellites and ground control stations (GCS) is fundamental to modern space missions, with its effectiveness being directly dependent on an unobstructed Line-of-Sight (LoS). Traditional site planning methods, relying on low-resolution terrain models, often overlook crucial obstacles like buildings or dense vegetation. This paper presents a comprehensive methodology using high-resolution Light Detection and Ranging (LiDAR) data, acquired from an Unmanned Aerial Vehicle (UAV), to precisely model horizon obstruction and optimize the placement of transceiver antennas. The methodology was verified on a real-world case study in Zielona Góra, Poland. The workflow included data acquisition, PPK-based trajectory processing, and point cloud subsampling using an Octree-based algorithm. The core of this work was the implementation of an algorithm to generate detailed elevation masks by calculating the maximum obstruction angle for defined azimuthal intervals. The analysis clearly identified the superior of two potential locations, proving the method's effectiveness as a decision-support tool in the space sector.

This paper investigates whether integrating the Microsoft Building Footprints (MBF) dataset with building footprints contributed by the OpenStreetMap (OSM) community can improve the spatial quality of building data. Specifically, the authors assess whether the resulting hybrid dataset enhances completeness and positional accuracy relative to the original MBF and OSM datasets. The evaluation was conducted in a study area encompassing both urban and rural environments, using 1:5,000 topographic cartography as the reference dataset. The merged MBF+OSM dataset successfully captured 87% of the buildings represented in the reference cartography, outperforming the standalone MBF and OSM datasets, which captured 81% and 70%, respectively. These results demonstrate that combining MBF and OSM footprints provides a more comprehensive representation of buildings and can offer a valuable alternative for applications requiring detailed, up-to-date building information.

The International Exposition of Turin held in 1911 in the Val-entino Park to celebrate the fiftieth anniversary of Italian unifi-cation; Hosted pavilions dedicated to science, industry, art, and architecture, symbolizing the modern spirit of post-unification Italy (Italy World’s Fairs, 2024). Today, only some traces of the project survive within the park. This disappearance has turned the exposition into a lost heritage landscape, known primarily through archival maps, photographs, and historical records. In late 2014 the project Turin 1911 started according to the cooperation between the Politecnico di Torino- Depart-ment of Architecture and Design in cooperation with the Uni-versity of California San Diego - School of arts and Humanities (https://italyworldsfairs.org/)

This project focuses on the digital revival of this exposition by integrating these materials with digital surveying and immer-sive visualization, aims to reproduce this vanished site and make it perceptible again to the public through virtual reality technologies.Within this framework, the research presented in this paper concentrates on the optimization of these mainly Revit and ArchiCAD modeled pavilions using the tools provid-ed by Unreal Engine for deployment on standalone VR sys-tems. The goal is to make heavy weight architectural scenes accessible in VR without connecting to PC and extending the concept to portable devices.

The integration of internet technologies and geographic information systems (GIS) has positioned Web Map Services(WMS) as indispensable tools for daily life, with their service quality attracting significant attention. This study proposes a quality evaluation model and method tailored for WMS, encompassing four critical dimensions: query and retrieval, map display, thematic services, and productization services. Empirical validation was conducted through functional, performance, and productization evaluations of three leading domestic platforms, utilizing technical benchmarks and user-centric metrics. Results demonstrate the model’s efficacy in quantifying service quality, aligning closely with real-world user experiences. The framework provides actionable guidelines for regulatory bodies to monitor service compliance and for providers to optimize architectural designs, thereby addressing gaps in personalization and cross-border functionalities observed in current systems. Furthermore, this work highlights the necessity of integrating emerging technologies—such as real-time traffic data and AI-driven personalization—to meet evolving demands for energy efficiency, global connectivity, and hyper-localized services. By bridging technical assessments with practical governance needs, the study offers a strategic roadmap for advancing service quality, supporting the development of China’s digital economy, and enhancing societal well-being through reliable geospatial solutions.

Virtual reality (VR) offers important advantages in training complex spatial skills, as required for example in driving, because it enables immersion and experience-based learning, and offers financial, ecological, and safety benefits. In the context of driving, as larger vehicles can be especially challenging to master for beginners, we investigated whether truck driving instruction and practice in a VR-simulator enhances performance, and whether the acquired skills transfer to maneuvering a real vehicle. In an empirical feasibility study, we first measured learners’ performance while an experienced instructor trained them on a conventional simulator vs. in VR, following analogous training protocols. The task was to reverse a truck with a trailer, a particularly difficult task that requires extensive practice. After training, participants completed a test on a real vehicle to validate the effectiveness of the training. Participants were asked to report previous experience, attitudes towards the system, motion sickness, and fatigue levels. Four male participants, who had a car driving license but no experience reversing a truck with trailer, completed the training. Results demonstrate that basic maneuvering skills can be trained in VR and transfer to the real vehicle. Even with a low-budget VR solution, participants learned easily, and learning curves were comparable to the simulator condition. Participants reported positive attitudes towards the training in both conditions. Future research could investigate whether using a customized VR environment that takes full advantage of all the benefits of VR, could lead to even greater training gains.

In recent years, the increasing frequency of natural disasters in remote and rugged areas has underscored the importance of unmanned aerial vehicles (UAVs) for rapid emergency response. This paper presents a novel approach for optimizing the placement of UAV docking stations in mountainous terrain for emergency operations. We develop a comprehensive, 3D Geodata framework that integrates 3D Digital Elevation Models (3D DEM), building infrastructure, and road network data to create a realistic three-dimensional optimization environment. The proposed system employs an Enhanced Adaptive Particle Swarm Optimization (EAPSO) algorithm with adaptive parameters, diversity maintenance mechanisms, and intelligent convergence detection to effectively handle the complex constraints of mountainous environments. Experimental results demonstrate that our 3D-aware EAPSO approach achieves superior performance in balancing coverage efficiency, energy consumption, and network connectivity compared to conventional optimization methods. The proposed system provides a scientific foundation for improving emergency response capabilities in challenging geographical environments.

The Sustainable development goals and the United Nations Ocean Decade require preservation of the oceans and the efficient management of marine resources, contributing to a sustainable oceans and blue economy. Oceans span a wide area with exclusive economic zones of different countries adjacent to each other. This therefore necessitates the collaborative management of these resources across several countries in a region. Data is essential to providing trusted information, which in turn drives knowledge generation from science to policy implementation, towards informed decision making regarding the ocean resources. Harmonising data into decision support tools becomes a challenge due to two main reasons. Firstly, due to the transdisciplinary nature of the ocean, where data is governed by a variety of standards. Secondly, regional collaboration requires the data and knowledge to be shared in a federated environment in order to preserve data sovereignty, while cognisant of the network challenges in developing countries. This paper presents a standard compliant framework for enabling data sharing and access in these environments based on lessons learnt in the Marine and Coastal Operations for Southern Africa and Western Indian Ocean region, a project supported by the African Union Commission‘s GMES and Africa program.

With the deep integration of big data and artificial intelligence technologies, the knowledge graph has emerged as an important method for organizing and understanding complex spatiotemporal information. Traditional knowledge graph management systems often face three significant challenges when dealing with spatiotemporal information in domains such as natural resource, urban studies, and emergency management. Firstly, the limited visualization capability makes it hard to intuitively represent the spatial distribution and temporal evolution of spatiotemporal knowledge. Secondly, the lack of systematic and deep machine-interpretable representation methods leads to inadequate diagnostic, predictive, and decision-making knowledge services. Thirdly, the knowledge construction process heavily relies on expert involvement resulting in high barriers to entry and low efficiency. To address these issues systematically, this paper designs and implements a comprehensive spatiotemporal knowledge graph construction and management system that integrates full lifecycle knowledge management, multi-form visualization methods, general and thematic knowledge graph construction.

Flooding is one of the most persistent natural hazards in Ghana, causing recurrent damage to infrastructure, livelihoods, and local economies. Despite its widespread impacts, most flood-related research has been concentrated on Accra, leaving many vulnerable regions understudied. This paper integrates Earth Observation (EO) datasets to identify and characterise flood-prone areas across Ghana at a national scale. Precipitation patterns between 2015 and 2025 were derived from the IMERG dataset, while Sentinel-1 Synthetic Aperture Radar (SAR) imagery was used for flood mapping through a change detection (ratio) approach. Results show a clear seasonal cycle, with major rainfall peaks from April to October, directly corresponding to observed flood events. Flooding is concentrated in the southern half of the country, particularly in Western, Western North and Eastern Regions, and recurrent hotspots around Kumasi in Ashanti and the Weija dam in Greater-Accra regions. Spatial patterns align closely with national topography, confirming the vulnerability of low-lying settlements and riverine communities. Technically, the study demonstrates the effectiveness of SAR-based change detection for flood mapping in data-sparse environments, while highlighting limitations related to in-situ validation and urban misclassification. From a policy perspective, the findings provide evidence to support flood risk management strategies, including targeted infrastructure investment and improved drainage planning. The results underline the necessity ofadopting engineering solutions to reduce flood vulnerability in communities in Ghana.

This contribution presents a feasibility study on representing access easement rights in multi-owned buildings using BIM and the IFC standard. A 3D BIM model was generated from 2D cadastral plans, and access easements between parking and storage units were modeled as explicit IFC entities with legal attributes such as beneficiary, servient unit, and restriction semantics. The study demonstrates how embedding easements as identifiable objects in IFC can enhance the clarity of Rights, Restrictions, and Responsibilities (RRRs) and improve the communication of legal constraints for future 3D digital cadaster applications.

The aim of the presented study is to develop and implement the strategy for digitally reconstructing and presenting the forgotten heritage associated with underground and open-pit mining conducted in the nowadays bilateral UNESCO Geopark Muskau Arch located on the border of Germany and Poland.

The research is led by scientific partners from Poland (Wroclaw University of Science and Technology) and Germany (Freiberg Technical University), with cooperation from the European Grouping of Territorial Cooperation (EGTC) Geopark Muskau Arch within the project “Digital Journey through Geopark Muskau Arch” co-financed from the European Regional Development Fund as part of the Poland-Saxony 2021-2027 INTERREG Cooperation Program.

Effective management of environmental safety risks in brownfield redevelopment relies on accurate identification of contaminated enterprises. A key challenge is the rapid acquisition of data on these enterprises. This study proposes a method leveraging Point of Interest (POI) data and a BERT-based prediction model to identify potentially contaminated enterprises. The method was applied to Datong, a major industrial and mining city in China. The method successfully identified 329 potentially contaminated enterprises across 23 types of polluting industries. Notably, enterprises in the mining and washing sectors of bituminous and anthracite coal represented 26.2% of the total identified, reflecting Datong’s coal-centric industrial nature. The proposed method efficiently identifies potentially contaminated enterprises, supporting targeted environmental risk management and brownfield redevelopment. Integrating it with regulatory frameworks can enhance compliance monitoring and inform decision-making for sustainable urban development.

The development of successful geospatial artificial intelligence (GeoAI) systems is hampered by two major obstacles: a scarcity of high-quality, annotated satellite imagery and a lack of unified platforms for modeling and testing. We introduce a scalable GeoAI framework that allows users to query, retrieve, and analyze high-resolution imagery using natural language interaction and direct processing of images. The system incorporates IBM-NASA's Prithvi Foundation Model for supervised detection of environmental features and the Clay Foundation Model for unsupervised similarity search when detectors are unavailable. An interactive interface allows users to search for features (such as swimming pools, vegetation changes, and burn scars), apply detectors to TIFF images, and explore new regions for model training

India, as one of the fastest-developing nations, faces severe air quality challenges due to rapid urbanization, industrialization, vehicular emissions, and agricultural activities. With major cities frequently exceeding WHO pollution limits, understanding the spatial and temporal behavior of atmospheric pollutants has become crucial. The integration of satellite-based geospatial technologies provides a powerful framework for assessing land–atmosphere interactions and their environmental implications. This study investigates the relationship between atmospheric pollutants and land surface characteristics across India using Sentinel-5P and Sentinel-2 datasets. The objective is to examine how pollutants influence vegetation health and urbanization through indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). Google Earth Engine (GEE) and MATLAB were employed for data processing, statistical analysis, and visualization. NDVI and NDBI were derived from Sentinel-2 bands, while pollutant data (NO₂, SO₂, CO, O₃, HCHO, and CH₄) were extracted from Sentinel-5P. Correlation analysis, univariate regression, and temporal trend models were used to evaluate pollutant behavior and its linkages with land cover dynamics from 2019–2024. Results revealed strong positive correlations among NO₂, CO, SO₂, and HCHO (r = 0.59–0.76), indicating common anthropogenic sources, while NDVI showed significant negative correlations with O₃ (r = –0.46) and HCHO (r = –0.64). Formaldehyde and methane displayed the strongest increasing trends, highlighting growing emissions and vegetation response contrasts. The findings emphasize the interconnectedness of pollution, vegetation degradation, and urban expansion. Future research should integrate meteorological parameters and predictive modeling to strengthen sustainable environmental management and urban planning frameworks in India.

Based on the current situation of the application and service of national fundamental surveying and mapping achievements from 2020 to 2024, this paper adopts a combined method of quantitative and qualitative analysis to identify the existing problems and challenges, including constraints imposed by confidentiality management policies, the need to improve the timeliness and category diversity of data, and the insufficient service awareness and informatization service level. Corresponding countermeasures and suggestions for promoting the efficient provision and extensive utilization of fundamental surveying and mapping achievements are put forward, mainly including improving the policy and institutional system for the confidentiality management of surveying and mapping achievements, perfecting the achievement update mechanism, enriching the variety of achievements, advancing the processing and compilation of public-version surveying and mapping achievements, and constructing a public geographic information data innovation and application laboratory.

This study addresses the impact of planar protective layers on stereo photogrammetry and introduces a rigorous refractive correction model based on multi-interface ray tracing. Conventional stereo reconstruction assumes a single viewpoint, but planar layers introduce refraction at two interfaces, causing systematic depth-dominated errors. Through simulations and field experiments using an Intel RealSense D455, the study evaluates the influence of target distance, layer thickness, orientation, and layer-to-camera spacing. Simulations with multiple target planes show that conventional stereo produces significant errors—up to several millimeters in depth—even for thin layers, while the refractive model consistently reconstructs points with sub-millimeter accuracy. Layer distance from the camera has negligible effect on the error magnitude, whereas tilts and thicknesses of the layer strongly influence the bias. Field experiments with a 10-mm acrylic plate confirm these findings: conventional reconstruction exhibits systematic lateral and depth errors, whereas the refractive model eliminates bias, achieving near-zero mean errors. The results highlight that even minimal protective layers induce measurable errors if refraction is ignored, emphasizing the necessity of refractive correction in high-precision applications. The study demonstrates that explicitly modeling refraction in stereo photogrammetry significantly improves reconstruction accuracy and robustness. Overall, this work provides a practical framework for accurate 3D measurement in hazardous environments where imaging through protective layers is unavoidable.

Urban digital twins increasingly require pedestrian-scale three-dimensional (3D) representations to support accessibility and inclusiveness assessment. However, existing approaches typically emphasize either geometric accuracy or visual realism, while lacking an integrated framework for analysing pedestrian-level conditions. This study proposes a hybrid workflow integrating handheld LiDAR and 3D Gaussian Splatting (3DGS) within a CityGML-based semantic framework for accessibility assessment. Handheld LiDAR provides centimetre-level geometric measurements, enabling the extraction of key indicators such as slope, surface roughness, and obstacle presence. In parallel, 3DGS reconstruction from 360° video imagery enhance visual realism and perceptual understanding. Both datasets are co-registered and structured within the CityGML 3.0 Transportation model to represent pedestrian environments in a unified spatial and semantic framework. Accessibility assessment was conducted using three approaches: LiDAR-based analysis, field survey observations, and immersive evaluation in a Virtual Reality (VR) environment. The LiDAR-based results were used as a reference. Comparative analysis shows the field survey assessment achieves an agreement of approximately 85.7%, while VR-based assessment reaches approximately 75.4%. The results indicate that while VR does not replace metric-based analysis, it enables perception-driven and participatory evaluation. In particular, VR-based assessment shows potential to involve users, including people with disabilities, in accessibility evaluation through immersive and remote interaction. The proposed approach contributes to the development of human-scale urban digital twins by integrating metric accuracy, semantic structure, and participatory evaluation for more inclusive accessibility analysis

Rapid urbanization and rising traffic demand are placing significant pressure on transportation infrastructure, necessitating more efficient and accurate approaches to road design auditing and asset management. Traditional survey methods are labor-intensive, time-consuming, and lack comprehensive three-dimensional context. This study presents an end-to-end framework integrating Mobile Light Detection and Ranging (LiDAR) with Artificial Intelligence (AI) for automated extraction of road geometric parameters and asset inventory. Mobile LiDAR data were collected along an urban corridor in Bengaluru, India, and preprocessed using Trimble Business Center. Preprocessing involved statistical outlier removal and progressive morphological ground segmentation. A deep learning model based on the PointNet++ architecture with hierarchical set abstraction layers was developed to classify point cloud data into five categories: road, pole, vehicle, tree, and building. The dataset comprised approximately 45 million points, with 10% manually annotated for training. The trained model enabled large-scale semantic segmentation, achieving a mean Intersection-over-Union (mIoU) of 0.86 and an overall accuracy of 92.4%. Using the classified outputs, key road design parameters—including lane width (8.099 m), road segment length (44.383 m), zebra crossing width (7.336 m), and pole height (7.890 m)—were accurately derived. The proposed workflow reduced manual processing time by approximately 85% (from 40 hours to 6 hours per km) while enhancing measurement consistency and scalability. The results highlight the effectiveness of integrating mobile LiDAR and AI for high-accuracy, data-driven infrastructure assessment, offering a scalable solution for improved planning and management of urban transportation systems.

Panoramic image stitching traditionally relies on the assumption that all input images share a single projection centre, a condition rarely satisfied by modern multi-camera rigs composed of multiple fisheye sensors mounted with non-negligible baselines. In confined or close-range environments, these geometric discrepancies introduce significant parallax, limiting the reliability of both classical and “parallax-tolerant’’ stitching techniques based on local warping. Although such methods are simple and efficient, they cannot account for the true camera geometry and therefore degrade the metric quality of the final panorama. At the same time, recent photogrammetric software has begun to accept panoramic imagery directly, yet literature demonstrates that optimal accuracy is still obtained when processing raw multi-camera.

This work presents a new 3D-informed approach for generating panoramic images that fully respects the underlying geometry of the acquisition system. Assuming the availability of a 3D model, derived either from photogrammetric reconstruction or from an external sensor such as LiDAR, the method reprojects each pixel of the desired panorama onto the original multi-camera frames using collinearity equations, mirroring the workflow of precision orthophoto generation. This allows the production of parallax-free panoramas with consistent geometric fidelity even in challenging scenarios.

The method is evaluated on several case studies using both compact panoramic cameras and multi-camera systems with larger baselines. Results demonstrate improvements in stitching accuracy, SfM orientation quality, and final 3D reconstruction, including robustness to varying scene complexity and supporting 3D-model resolution.

Recovering editable CAD models from point cloud scans is a key challenge in reverse engineering and quality control, where the ability to reconstruct the original modeling history of a physical object enables precise deviation analysis and systematic process optimization. While deep learning has driven significant progress in this area, existing models struggle to generalize to complex CAD models, which feature multiple extrusions and intricate geometric structures.

This paper presents an end-to-end deep learning pipeline that reconstructs CAD models from point clouds as structured CAD sequences, which are series of sketch-and-extrude operations that encode the full modeling history. The model demonstrates high-fidelity reconstruction for non-complex objects, including primitive shapes such as cubes and cylinders, as well as their assemblies.

To address the performance gap on complex shapes, we introduce an extrusion-based segmentation strategy that decomposes CAD models into their constituent extrusions. These partial shapes are incorporated into the training set, increasing data diversity without requiring new data collection. The resulting primitive models feature partially occluded point clouds, surfaces hidden in the original assembly are absent, which forces the model to infer missing regions and learn richer point cloud representations. This increases the complexity of the reconstruction problem and thereby improves generalization.

The strategy is model-agnostic and can be applied to any deep learning approach that reconstructs CAD sequences, making it a broadly applicable tool for the community.

Integration of LROC NAC and ShadowCam imagery is essential for meter-scale controlled mapping of the entire lunar south pole including Permanently Shadowed Regions (PSRs), but remains challenging due to extreme radiometric differences, sparse overlap across illumination boundaries, and ill-conditioned bundle adjustment networks. This paper proposes a LOLA DEM-mediated multi-source bundle adjustment framework for controlled lunar polar mapping. A hierarchical cross-modality matching strategy is developed using first- and second-order Gaussian steerable gradient features with multi-scale fusion and phase-correlation-based subpixel refinement. Sensor-specific geometric models are established using second-order polynomial transformations for NAC orthoimages and rational polynomial models for ShadowCam map-projected images. Five types of geometric constraints are formulated to integrate intra-sensor, limited cross-sensor, and image-to-DEM observations, with the LOLA DEM acting as a common geometric mediator. To stabilize the heterogeneous network, a hybrid L1-L2 regularization model with adaptive two-stage weighting is optimized using ADMM algorithm. Experiments in the lunar south polar region demonstrate substantial improvements on intra-sensor, cross-sensor, and image-to-reference positioning accuracy. The final seamless 1 m/pixel orthorectified mosaics achieve approximately 5 m absolute accuracy, validating the proposed framework for geometrically unifying illuminated and permanently shadowed terrain in lunar polar controlled mapping.

National LiDAR programs are increasingly adopted worldwide to support land management, infrastructure planning, and environmental monitoring. Following the examples of large-scale initiatives in the United States and Europe, Italy launched its first nationwide LiDAR survey in July 2025 within the Integrated Monitoring System (SIM) project funded by the National Recovery and Resilience Plan (PNRR). This effort represents the most extensive airborne LiDAR campaign ever conducted in the country, covering over 302,000 km², including coastal zones and major islands. The acquisition plan is designed to ensure a minimum point density of 10 points/m² and produce high-resolution DTMs and DSMs at a 0.25 m grid spacing.

Given the unprecedented spatial and data volume, a robust, standardised, and fully automated quality assurance framework is essential. This paper presents the methodology used to evaluate geometric consistency and spatial accuracy across the national dataset. Congruence between overlapping flight strips is assessed by automatically extracting 100 × 100 m patches at regular intervals and computing point-to-point distances and cross-section profiles to detect horizontal and vertical discrepancies. Plano-altimetric accuracy is further evaluated through comparisons with terrestrial laser scanning (TLS) data collected in dedicated control areas, where robust plane fitting enables rigorous three-dimensional error estimation.

Results from two control areas acquired with different sensors demonstrate the effectiveness, scalability, and reproducibility of the proposed automated workflows. The presented approach provides a reliable foundation for delivering high-precision national LiDAR products and offers a framework applicable to future large-scale geospatial acquisition programs.

The study explores a workflow for integrating photogrammetric image blocks with LiDAR point clouds acquired via Unmanned Laser Scanning (ULS) in forestry applications. Hybrid datasets combining UAV imagery and LiDAR data are increasingly used for 3D mapping, yet discrepancies often arise due to independent orientation processes and systematic errors. Traditional solutions rely on numerous ground control points (GCPs), which can be impractical in dense forest environments. To address this, the proposed method fuses Bundle Adjustment and Iterative Closest Point (ICP) algorithms in a joint optimization process, aligning multispectral images with ULS point clouds without additional observations or GCPs. The workflow includes a GPU-accelerated filtering step to extract representative canopy points, reducing computational load and improving correspondence selection. Implemented using Python and C++ extensions, the system leverages the Ceres Solver for non-linear optimization, minimizing reprojection, GNSS, IMU, and point-to-cloud errors iteratively. Tests conducted in Żednia Forest District, Poland, during leaf-on and leaf-off seasons demonstrated significant improvements in alignment accuracy: average horizontal errors decreased by over 50%, and maximum offsets were reduced by more than 1 meter. These results confirm that the proposed hybrid adjustment substantially enhances geometric consistency between photogrammetric and LiDAR datasets, offering a cost-effective solution for forestry mapping and monitoring.

This paper introduces an innovative Virtual Reality (VR) serious game designed to enhance immersive learning in cultural heritage education. The game offers an interactive exploration of the Holy Aedicule in Jerusalem, one of the most sacred monuments of Christianity, based on high-resolution 3D documentation captured before, during, and after its rehabilitation.

By integrating photogrammetric data, textured 3D models, and historical research, the application allows users to navigate the monument virtually, engage with embedded educational content, and participate in interactive learning scenarios. Structured as a multi-phase experience, including virtual tours, a digital classroom, and a quiz mode, the serious game aims to promote transdisciplinary knowledge transfer in a user-friendly, entertaining format.

This contribution outlines the game’s methodological framework, educational objectives, development pipeline, and user evaluation results, highlighting its role in redefining how cultural heritage can be communicated through immersive digital tools. Additionally, it addresses the broader challenge of translating complex heritage documentation into accessible and meaningful experiences for learners, researchers, and the wider audience.

The contribution investigates the possibility of using a GNSS receiver equipped with a camera for documenting built heritage. In particular, the possibility of measuring GCPs on vertical surfaces thanks to the combination of satellite observations and digital photogrammetric algorithms will be analysed and metrically validated. Moreover, the use of the acquired images in SfM approaches will be tested and discussed.

Military vehicle recognition faces critical data scarcity due to operational security constraints and prohibitive collection costs.

Classification of vehicles demands extensive training data rarely available in defence contexts. We propose a hybrid approach

combining limited real-world data with scalable synthetic generation. Our methodology comprises: (1) a Blender-based pipeline

generating high-resolution synthetic images with domain randomization across 3D models, lighting, and camera angles; (2) training

transformer-based RF-DETR detectors on real-world and synthetic data, respectively; (3) an in-depth evaluation of the trained

networks to determine the effect of synthetic data. Our approach utilizes a baseline RF-DETR detector trained on real-world

imagery to compare against. Then we utilize the custom-made synthetic data generation pipeline to create an equally large synthetic

dataset. This generated data is added to real data subsets, thus creating a mixed datasets containing varying percentages of real data.

We created five datasets containing 5%, 10%, 25%, 50%, and 100%, respectively. With these new mixed datasets we train another

set of RF-DETR detectors. Afterwards we evaluate the influence of the synthetic data by comparing the detectors across computer

vision metrics.

Semantic segmentation of outdoor 3D point clouds degrades significantly under adverse weather, as rain, fog, and snow corrupt the geometric structure of LiDAR returns through backscatter insertion, range-dependent attenuation, and volumetric scattering. Existing domain generalization methods constrain feature values directly, which becomes less effective when weather-induced perturbations alter the local neighborhood topology that underlies feature aggregation. This work proposes Geometric Diffusion Consistency (GDC), a training-time regularizer that enforces consistent feature propagation behavior across geometrically divergent views of the same point cloud. A dual-view augmentation pipeline generates training pairs through weak and strong perturbations, where the strong branch incorporates dual-mode atmospheric extinction modeling, semantic-aware geometric corruption, and weather-coordinated structural perturbation. A lightweight learnable diffusion operator, implemented via sparse convolutions with a gated residual connection, propagates encoder bottleneck features through local voxel neighborhoods. The consistency loss aligns diffused representations at corresponding points across views, preserving topological relationships essential for dense prediction while allowing feature values to adapt to altered geometry. On the SemanticKITTI to SemanticSTF domain generalization benchmark, GDC achieves 38.6% mIoU, exceeding the previous best method by 3.8%, with consistent improvements across dense fog, light fog, rain, and snow conditions.

The project 'Stone Witnesses Digital' ensures the exemplary documentation of a selected number of German Jewish graveyards. This paper presents the results from the first years of the project’s geomatics work, including the development of an integrated multi-sensor workflow for 3D imaging—ranging from geographic-scale documentation of entire graveyards (1:200 scale) to detailed feature imaging of individual gravestones (1:20 scale). The workflow supports the long-term research project on Jewish sepulchral culture "Stone Witnesses Digital".The project brings together expertise from Jewish Studies, Digital Technologies in Heritage Conservation, and Historic Building Research.

The overarching scope is to document the location and context of gravestones, their materiality, decorative elements, inscriptions, and the meanings embedded within them—summarized under the guiding concept 'Space, Form, Inscription.' The aim of the project is to create a comprehensive digital dataset that documents inscriptions as well as the spatial and structural characteristics of gravestones, thereby ensuring their long-term preservation and making them accessible for further academic research.

To achieve this, the work-flow must integrate various sensing and 3D imaging techniques, ensure reliable and sustainable data storage, and support reproducible dataset creation for spatio-temporal analyses and long-term monitoring of grave-yards throughout the 24-year project period. It also enables the combination of advanced sensing technologies with semantic web standards and facilitates the creation of informative Open Access outputs compliant with FAIR data principles.

The research presented in this abstract is a novel, low-cost pipeline for the semi-automated 3D reconstruction of reindeer antlers using an optical camera array and Gaussian Splatting (GS). Traditional antler measurement methods are manual, invasive and prone to errors, while existing 3D scanning techniques struggle with subject motion. Photogrammetric bundle adjustment derived point clouds require well defined points which are generally lacking on antlers. To overcome this a system of 16 synchronized Raspberry Pi cameras was used to capture instantaneous imagery within an animal enclosure. A sparse point cloud along with the oriented network of imagery from a bundle adjustment is fed into a GS algorithm, producing an optimized reconstruction of the scene.

The system was initially validated in a controlled lab environment against a terrestrial laser scanner ground truth point cloud. A sub-centimeter accuracy with mean cloud-to-cloud distance of 4.0mm was achieved. Preliminary live-animal testing demonstrates the systems ability to produce a qualitatively accurate reconstruction under various lighting conditions. This method establishes a non-invasive method for high quality 3D reconstructions of complex reindeer antlers, which has applications in wildlife biology, environmental monitoring and biomechanics. Further work will involve rigorous network and camera calibration along with a comprehensive analysis of live-animal data.

Automatically generating architectural plans from archaeological sites poses a persistent challenge, particularly when dealing with ancient structures that have experienced severe deterioration. Many heritage contexts—especially those involving rock-cut monuments—present highly irregular geometries, collapsed features, eroded walls, and surfaces obscured by sediment or plaster detachment. These conditions make the extraction of reliable 2D plans or cross-sections from 3D data exceptionally difficult using conventional modeling tools.

In this study, we propose a semi-automated processing workflow tailored to the architectural characteristics of the Sheikh Said tombs. The pipeline converts 3D LiDAR datasets into structured 2D plans and vertical cross-sections, with particular emphasis on documenting deep, narrow shafts and multi-chambered tomb layouts.

The generation of Level of Detail 3 (LoD3) building models is essential for applications such as urban digital twins, energy analysis, and smart city planning. However, conventional approaches based on terrestrial LiDAR or UAV photogrammetry remain costly, labor-intensive, and difficult to scale. This paper presents a scalable framework for transforming LoD1 building models into LoD3 façade representations using openly available urban data, including OpenStreetMap footprints, street-level spherical imagery, and weak point-cloud priors. The proposed method formulates the reconstruction problem as a facet-based modeling task, where each façade is processed independently in a local coordinate system derived from LoD1 geometry. A rectification strategy is introduced to generate fronto-parallel façade images directly from spherical panoramas, avoiding perspective distortions and facilitating image analysis. To address the challenges of unstructured data acquisition, a visibility-driven view selection scheme and a multi-view fusion framework are developed to construct robust façade evidence maps. The 3D geometry is estimated as a depth field through a multi-resolution optimization framework integrating ray consistency, appearance cues, point-cloud support, and structural regularization. Planar segmentation, polygonization, and geometric regularization are subsequently applied to derive structured façade elements. Openings such as windows and doors are detected using combined geometric and image-based evidence and further refined through architectural constraints. Experimental results demonstrate that the proposed framework enables reliable reconstruction of façade geometry and structural details using only open and low-cost data sources, providing a practical pathway for large-scale LoD3 generation in real urban environments.

Large-scale LiDAR point clouds provide rich geometric information, yet learning effective structural representations remains challenging due to the misalignment between semantic categories and geometric structures. To address this issue, we propose a SAM-guided framework for point cloud oversegmentation. We transfer grouping knowledge from 2D vision by constructing a large-scale oversegmentation dataset using the Segment Anything Model (SAM) on bird’s-eye-view projections.

Based on these grouping priors, a structure-aware point cloud encoder is learned via a distillation objective that enforces intra-region compactness and inter-region separation in the embedding space. The proposed approach does not rely on semantic supervision and directly learns generalizable structural representations.

Experiments on various benchmark datasets (STPLS3D, Toronto-3D, DALES, and S3DIS) demonstrate that the proposed method achieves competitive performance.

In particular, it significantly improves boundary recall (e.g., 92.21% on STPLS3D and 93.47% on Toronto-3D) while maintaining high oracle accuracy (up to 97.62%).

Moreover, the model generalizes well to unseen datasets without retraining, showing strong cross-dataset inference capability.

In this work we propose an object-specific implicit representation: Functional modeling of surface geometry using Gaussian Processes (GPs). n contrast to neural models, our method leverages the ability of GPs to model continuous functions from irregularly sparse sampled data and apply this concept in the context of a probabilistic model that learns the shape of an object as the mixture of multiple directional distance fields anchored at reference points specially placed in the object’s skeletal outline. The resulting mixture model provides continuity, sparsity, and finer shape detail while avoiding the heavy training burden associated with deep implicit methods

Accurate tree species identification is essential for effective forest management, biodiversity monitoring, and resource estimation. While automated methods relying on aerial and canopy-level remote sensing have become prevalent, they often struggle in dense, multi-layered forest stands, where critical lower-stem and bark features are obscured. To address this limitation, we present a Deep Learning (DL) framework for tree species classification utilizing ground-level RGB imagery. Because manual annotation of terrestrial images in forest environments is labor-intensive and complicated by occlusions, we introduce a new "in-situ" forest image dataset alongside an automated labeling pipeline. This pipeline generates training annotations by projecting tree-species data derived from Mobile Laser Scanning (MLS) onto 2D images based on photogrammetric reconstruction. The proposed DL model leverages these automatically labeled images to effectively recognize tree species based on structural and bark characteristics. The model achieves overall F1-scores of 0.78 and 0.75 for object detection and instance segmentation, respectively. Ultimately, our approach complements existing methods for detecting tree positions and diameters, facilitating the creation of a holistic, cost-effective, and scalable forest inventory dataset.

Historical masonry hydraulic infrastructures are affected by complex degradation processes, including vegetation growth, moisturerelated anomalies, and salt efflorescence, whose detection requires non-invasive, repeatable, and scalable diagnostic approaches. This study proposes a multi-scale workflow for the detection and classification of degradation phenomena affecting the Cavour Canal water bridge, a nineteenth-century masonry structure in northern Italy. The methodology integrates UAV-based multispectral orthophotos and close-range hyperspectral imagery within a common Object-Based Image Analysis (OBIA) framework. The multispectral workflow was designed for façade-scale screening, whereas the hyperspectral workflow was used to refine the interpretation of selected sectors through detailed spectral characterisation. Multiple supervised classifiers, including Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Decision Tree (DT), Random Trees (RT), and Naïve Bayes (NB), were tested on both datasets. The results show that the multispectral workflow is effective for the identification of vegetation and broad water-related anomalies, with kNN providing the best overall performance, while the hyperspectral workflow improves the discrimination of subtle surface alterations, particularly efflorescence, with SVM yielding the most stable results across the tested configurations. Overall, the proposed methodology demonstrates the value of integrating multispectral and hyperspectral data within a hierarchical workflow for non-invasive degradation mapping of historical masonry infrastructures.

The increasing demand for accurate forest inventory in tropical ecosystems requires robust, scalable methods for individual tree segmentation. Tropical forests pose particular challenges due to dense understory, high species diversity, and complex multi-layered canopies, which often lead to tree under- and over-segmentation in LiDAR-based workflows. This study presents a general framework for individual tree segmentation from dense, multi-resolution LiDAR point clouds acquired by a Backpack LiDAR system over a 15-year-old palm stand in Belém, Brazil. After trajectory enhancement and mapping, an adaptive cloth simulation filter is used to derive a Digital Terrain Model and height-normalized points. Woody components are then isolated using Otsu-based intensity thresholding, eigenvalue-derived linearity, and statistical outlier removal. Trunk detection combines DBSCAN clustering on lower-stem points with a dual tree-localization strategy based on sum-of-elevation heat maps and RANSAC circle fitting. A segmentation quality-control module addresses over- and under-segmentation before reattaching canopy and foliage via voxel-based KD-tree retrieval to generate final per-tree segments. Compared with 3DFIN and TreeLearn using point cloud–derived reference tree locations, the proposed framework achieves a precision of 92.85%, recall of 95.97%, and F1-score of 94.38%, substantially outperforming 3DFIN (75.97%) and TreeLearn (15.14%). These results demonstrate the potential of the proposed framework to deliver reliable tree-level inventories in complex tropical forests.

Historical surveying instruments embody centuries of innovation in cartography and engineering, serving as crucial scientific and pedagogical artifacts. Their fragility, risk of damage, and limited exhibition space restrict access and highlight the need for effective preservation strategies (Duester, 2023). Traditional conservation methods protect material integrity but do not address broader challenges related to accessibility and engagement. Digital technologies now offer transformative alternatives capable of creating accurate and interactive representations of these instruments (Farella et al., 2022).

This study proposes a low-cost, replicable digital preservation pipeline integrating close-range photogrammetry and augmented reality (AR). Photogrammetry provides a non-contact method for generating detailed 3D models using consumer-grade smartphones, democratizing access to advanced documentation techniques (Icardi et al., 2018; Förstner & Wrobel, 2016). AR enables users to interact with these digital surrogates in real environments, fostering deeper engagement and overcoming limitations imposed by fragile originals (Spallone, 2022; Gong et al., 2022).

Image acquisition was conducted with a Xiaomi Poco F5 Pro under controlled lighting, maintaining 30–60% overlap. Processing in Agisoft Metashape included alignment, dense cloud generation, mesh reconstruction, and texturing. Post-processing in Blender optimized the models for real-time visualization. Integration into AR was achieved using Unity and the Vuforia Engine SDK.

Results demonstrate high-fidelity 3D models that preserve fine details and offer immersive AR interaction. This pipeline provides durable digital records, enhances educational experiences, and expands public access. The approach aligns with ISPRS Working Group II/6 objectives and offers a scalable model for cultural heritage institutions seeking accessible and effective preservation strategies.

Learning-based methods for 3D scene reconstruction and object completion require large datasets containing partial scans paired with complete ground-truth geometry. However, acquiring such datasets using real-world scanning systems is costly and time-consuming, particularly when accurate ground truth for occluded regions is required.

In this work, we present a virtual scanning framework implemented in Unity for generating realistic synthetic 3D scan datasets. The proposed system simulates the behaviour of real-world scanners using configurable parameters such as scan resolution, measurement range, and distance-dependent noise. Instead of directly sampling mesh surfaces, the framework performs ray-based scanning from virtual viewpoints, enabling realistic modelling of sensor visibility and occlusion effects. In addition, panoramic images captured at the scanner location are used to assign colours to the resulting point clouds.

To support scalable dataset creation, the scanner is integrated with a procedural indoor scene generation pipeline that automatically produces diverse room layouts and furniture arrangements. Using this system, we introduce the V-Scan dataset, which contains synthetic indoor scans together with object-level partial point clouds, voxel-based occlusion grids, and complete ground-truth geometry. The resulting dataset provides valuable supervision for training and evaluating learning-based methods for scene reconstruction and object completion.

3D Gaussian Splatting (3DGS) has emerged as a promising method for photorealistic scene reconstructions, yet its application to semantic segmentation in real-world heritage documentation remains underexplored. This study proposes and evaluates an automated semantic 3DGS segmentation pipeline integrating the Segment Anything Model 3 (SAM 3) with per-class prompting for Gaussian reconstruction, applied to a nadiral UAV dataset of the Siti Inggil heritage complex in Cirebon, Indonesia. Segmentation performance of four semantic classes (ground, roofs, vegetations, and water bodies) were assessed against manually segmented 2D and 3D reference data, supplemented by geometric accuracy assessment via the M3C2 analysis. Results reveal both the promise and the inherent challenges of applying 3DGS segmentation to complex real-world heritage scenes, where acquisition geometry, surface characteristics, and foundational model limitations can be observed.

This paper addresses the critical challenge of early and accurate detection of piping, a major failure mode in levee systems. Traditional methods are limited, and even advanced techniques such as infrared thermography struggle to capture weak thermal anomaly signals under complex environmental interference. To overcome these limitations, we propose an innovative intelligent algorithm that achieves breakthroughs by synergistically integrating drone-based infrared imagery and point cloud data.

The methodology follows a rigorous two-stage pipeline. First, potential piping zones are coarsely extracted from thermal infrared images using an enhanced saliency detection model. This involves superpixel segmentation and multi-scale (global and local saliency) analysis to highlight temperature anomalies, followed by adaptive thresholding based on Gaussian distribution fitting for automatic segmentation. Second, a fine discrimination step is introduced, which integrates multimodal prior information from point clouds to significantly reduce false alarms. This is achieved by applying a series of physical constraints: area filtering, temperature variance filtering, terrain-based filtering, and overlap analysis between the infrared and point cloud data.

Validation with field data collected during the flood season demonstrates that this method achieves high-precision localization of piping zones. Its key advantage lies in its ability to effectively suppress false positives caused by environmental clutter while ensuring that the detection results align with physical principles. This study provides a practical and reliable technical solution for enhancing the safety inspection and early warning systems of levee structures.

This paper presents Mesh2Tile, an open-source pipeline that converts photogrammetric meshes into runtime-optimized 3D Tiles for interactive visualization in game engines. Photogrammetry produces high-polygon meshes that remain difficult to deliver at scale

through interactive platforms. Cloud-based conversion services like Cesium Ion provide a path to the OGC 3D Tiles format but impose cost barriers and raise data sovereignty concerns for confidential heritage projects. Existing open-source converters rely on uniform spatial partitioning, export redundant textures with every tile, and offer limited control over LOD generation. Mesh2Tile leverages Blender's Python API to perform adaptive octree tiling driven by triangle density, per-tile texture baking that eliminates texture redundancy, and parallel processing to generate georeferenced 3D Tiles from OBJ meshes. The pipeline is validated through a case study of the Bytown Museum Commissariat Building on the Rideau Canal UNESCO World Heritage Site. It is processed at three scales from 900 thousand to 90 million triangles. Results demonstrate linear scaling of processing time, up to 62% file size reduction for larger models, and successful runtime streaming in Unreal Engine 5 through the Cesium for Unreal plugin at 120 FPS with comparable tile balance to Cesium Ion's commercial output. The pipeline enables institutions to maintain full control over sensitive heritage data while achieving performance suitable for interactive visualization.

This contribution presents a method to determine ground coordinates of pedestrians from a single CCTV frame using monocular depth estimation and orthophoto-based ground control points. Urban crowd monitoring requires pedestrian location information, but many CCTV-based approaches rely on accurate camera calibration or multi-view configurations, which are often unavailable in real deployments. In this study, we exploit relative depth values from a monocular depth estimation model (Depth Anything V2) and ground control points jointly identifiable in both the CCTV frame and an orthophoto in EPSG:5186. For each frame, depth-based distance ratios between the pedestrian and ground control point pairs are used to construct Apollonius circles in the orthophoto plane, and the pedestrian position is estimated by a weighted least-squares adjustment of their intersections. The method is evaluated on 180 frames across two scenes from an urban testbed with camera–target distances within approximately 50 m, across three GCP placement scenarios. For the optimal configuration (Scenario A), a mean RMSE of 1.989 m was achieved, excluding frames in which GCPs were temporarily occluded by moving objects, demonstrating that single-frame CCTV imagery combined with an orthophoto can achieve an accuracy of approximately 2 m without any EOP/IOP information, which is practically useful for urban crowd monitoring and dynamic thematic mapping. The influence of GCP placement geometry and occlusion conditions on estimation accuracy is also analyzed

With the rapid advancement of multi-sensor technology, cross-modal image matching has become a key research focus. However, significant challenges persist, primarily caused by differences in imaging mechanisms that lead to nonlinear radiation variations and feature heterogeneity.Coupled with complex geometric distortions, traditional feature description methods in matching struggle to directly or effectively represent common feature information across modalities, resulting in matching failures. Thus, effectively mitigating noise and radiation distortions to enable robust cross-modal matching remains an open and critical problem, compounded by the intrinsic difficulty of balancing descriptor parameters like patch size and histogram partitioning. To address the aforementioned issues, this paper proposes a novel Multi-Level Multimodal Invariant Feature Descriptor (ML-MIFD), designed to enhance resistance to nonlinear radiometric differences and multi-source noise while maintaining rotation invariance. The proposed algorithm consists of three stages: feature detection, ML-MIFD descriptor construction, and image matching.This paper conducts comparative experiments with various state-of-the-art methods using typical cross-modal image datasets. The results demonstrate that the ML-MIFD method exhibits significant advantages in both registration accuracy and matching stability.

Mining represents a strategic activity for economic development; however, this activity causes significant impacts on the landscape, soil, and water resources. During the restoration phase, slope erosion represents a challenge for ensuring the geomorphological stability and ecological functionality of the affected areas. This study aims to evaluate the erosion dynamics of restored mining slopes by integrating Geographic Information Systems (GIS) and data obtained from Unmanned Aerial Systems (UAS) for geomorphological monitoring and quantification of soil loss on slopes. The research was carried out at the Lázaro quarry, Tarragona, Spain, using a fixed-wing UAS equipped with a multispectral camera to generate high-resolution orthophotos and Digital Elevation Models (DEMs), and compared with historical LíDAR data. Height Difference Models (HDMs) and volumetric analysis were applied to quantify erosion and deposition processes. Three modelling approaches were compared: ridge-derived DEM (DEMp), filtered DEM (DEMf), and lidar DEM (DEMl), considering their accuracy, spatial detail, and ability to represent erosional microtopography. The findings revealed that the DEMp provides the most consistent estimates of volume loss and most faithfully reproduces pre-erosion morphologies. At the same time, the DEMf tends to smooth relief, while the DEMl provides a lower-resolution overview. These results confirm the effectiveness of integrating UAS data, photogrammetry, and geospatial analysis for monitoring restored slopes, enabling the accurate quantification of eroded volumes and the detailed characterisation of morphological processes. This study contributes to the optimisation of the geomorphological and environmental management of restored mining areas, promoting their long-term stability and sustainability.

This submission presents the results and analysis of the SfM application for the processing of historical aerial VHS videos. The test data was collected during the 1997 Central European Flood and poses significant challenges due to the low quality of the data, the manner of the data acquisition (corridor mapping from different altitudes), and the object (a significant part of the images show the water). The SfM processing was executed in commercial software and allowed for successful image block bundle adjustment and creation of subsequent products, such as dense point cloud and orthomosoaics. One of the challenges during processing was the extraction of the approximate position of images and the selection of processing parameters.

Satellite imagery acquired over large areas from multiple viewpoints introduces subtle geometric misalignments that degrade the quality of derived products such as Digital Surface Models (DSMs). This paper presents a global block adjustment workflow designed to correct these errors across overlapping stereo acquisitions from the CO3D constellation, which captures Earth's surface at 50 cm resolution.

The proposed pipeline operates in three stages: individual acquisition refinement using Space Reference Points (SRPs) as Ground Control Points; tie point extraction between overlapping scenes through two-pass image correlation; and a weighted global spatio-triangulation simultaneously optimizing attitude biases, attitude drifts, and per-satellite magnification parameters.

Applied to a large stereo acquisition dataset over the Aorounga crater, Chad, the method demonstrates strong geometric performance. The results highlight that careful parameterization — combining observation weighting, n-tuple point filtering, and per-satellite sensor refinement — is key to producing accurate, geometrically consistent large-scale mosaics from bi-satellite stereo imagery. This paper does not include the in-orbit performances due to confidentiality agreement.

Accurate tree mapping in semi-urban areas is essential for ecological monitoring and infrastructure maintenance, but is challenged by complex structures and clutter in LiDAR data. This study proposes a learning-based framework using a Superpoint Transformer (SPT) for semantic segmentation. The model is pretrained on the KITTI-360 dataset and then fine-tuned using transfer learning on a high-resolution dataset captured by our in-house Bike-Pack LiDAR system. A key contribution of this work is a context-based quality control process applied after the initial segmentation. This quality control process refines the results by removing building artifacts, correcting misclassifications between vegetation and poles using geometric and intensity analysis, and refining building boundaries. Experiments demonstrate that this QC process significantly improves segmentation accuracy, especially for the critical vegetation and pole classes.

Posidonia oceanica (PO) meadows represent one of the most valuable coastal ecosystems in the Mediterranean Sea, providing key ecological functions and ecosystem services (Vassallo et al., 2013). Even after detachment, PO leaves and rhizome fragments accumulate along the shoreline forming thick deposits known as banquettes (Rotini et al., 2020). These natural structures play a crucial role in protecting beaches from erosion, buffering wave energy, and contributing to the nutrient cycling of coastal systems (Fonseca and Cahalan, 1992).

Despite their ecological importance, banquette dynamics are not consistently monitored, standardized monitoring procedures are lacking, and their spatial and temporal variability remains poorly understood. Within the framework of the POSEIDON project, funded by the Italian National Recovery and Resilience Plan (PNRR), innovative high-resolution mapping techniques are being developed to monitor PO ecosystems both underwater and on the coast. This contribution presents a methodology based on UAV RGB photogrammetry for the multitemporal analysis of banquette morphodynamics, demonstrating its potential for quantitative assessment of seasonal and interannual changes. UAV photogrammetry has become a widely adopted tool for high-resolution coastal monitoring and topographic mapping, providing centimeter-scale DEMs when combined with RTK positioning and well-distributed ground control points (Zannutta et al., 2020; Vecchi et al., 2021; Yoo and Oh, 2016).

This paper presents a comparative analysis of traditional photogrammetric methods and 3D Gaussian Splatting (3DGS) technology in the digitisation of Cultural Heritage (CH). Two representative datasets, differing in scale and image acquisition conditions, were selected to systematically evaluate the performance of both methods in terms of visual quality, geometric accuracy, computational efficiency and stability. The results indicate that 3DGS significantly outperforms traditional photogrammetry methods in terms of rendering quality and real-time visualisation capabilities, generating more realistic and immersive visual effects. However, its geometric accuracy is generally slightly lower than that of traditional methods, a difference that is particularly pronounced in small-scale datasets or under low-resolution input conditions. Among the various implementation methods, Postshot and LichtFeld Studio demonstrated higher stability and robustness, whilst the original GraphDeco method exhibited greater sensitivity to data scale and parameter settings. Photogrammetry offers reliability in high-precision geometric reconstruction, whilst 3DGS demonstrates significant potential for complementing this with a high-fidelity visual experience. The research findings try to provide practical guidance for selecting 3D reconstruction methods across different cultural heritage application scenarios.

Understorey vegetation (USV) contributes to forest structure, nutrient cycling, species diversity, habitat functions, and disturbance processes in Fennoscandian forests. It also provides non‑wood forest products such as wild berries. Mapping USV is important for understanding ecosystem functioning and its links to overstorey conditions. Although remote sensing (RS) enables large‑scale forest monitoring, its use for USV mapping remains limited because the layer is often obscured by upper‑canopy foliage. This study assesses the accuracy of USV cover prediction (i.e., the ground area covered by USV) using multiple RS data sources, identifies key predictors, and evaluates how canopy cover influences model performance. Field data were collected in 2024 from 487 plots in the Krycklan catchment. Sentinel‑2 summer and autumn imagery provided spectral reflectance, spectral indices, and grey‑level co‑occurrence matrix (GLCM) texture variables. Additional texture variables were derived from canopy height models (CHMs) generated using airborne laser scanning (ALS; 1–2 points/m²) and Pléiades tri‑stereo image matching (0.5 m; 1.5 points/m²). Beta regression and random forest regression (RFR) models were trained on 70% of plots and validated on 30%. Important predictors included seasonal red‑edge differences, greenness‑based indices, CHM texture variables, and ALS‑based canopy cover. Model performances indicated obstruction due to overstorey canopy cover remains for USV cover prediction. Beta regression with Sentinel‑2 data performed slightly better (RMSE = 21.7 m², variance explained = 5%) than RFR. However, best results occurred in low‑canopy plots (≤40%) using RFR with Sentinel‑2 and Pléiades‑derived CHM texture variables (RMSE = 14.6 m², variance explained = 32%).

Well logging curves play a crucial role in oil and gas exploration and geological engineering, as they provide essential information about subsurface formations and reservoir properties. In recent years, with the growing adoption of deep learning techniques in geoscientific data analysis, well logging data have increasingly been modeled as depth-dependent sequences, enabling the application of sequential neural networks for their analysis. Among these approaches, attention mechanisms have been adopted in log interpretation tasks due to their ability to capture long-range dependencies within sequences. However, directly applying attention mechanisms without considering the intrinsic structure of logging data may introduce model redundancy and increase learning complexity, which can ultimately degrade predictive performance. To address this issue, this study proposes a Sequence-based Decoupling Encoder (SDE). The proposed encoder explicitly disentangles the interactions between logging curves and across depth, enabling the model to learn relationships along different dimensions separately, which allows more effective feature extraction and mapping into a latent space. The decoupling strategy also reduces the learning complexity of the attention mechanism and provides clearer learning objectives for the model. The proposed method is evaluated on the public dataset \textit{FORCE2020} and applied to two common well log interpretation tasks: missing log reconstruction and lithology prediction. We compare SDE against several representative sequential baselines. Experimental results demonstrate that SDE achieves superior predictive performance in both tasks.

Handheld LiDAR systems are emerging as a promising alternative to traditional terrestrial and airborne laser scanning for environmental research, yet their performance and applicability remain insufficiently explored. The Mandeye LiDAR device, developed between 2022 and 2024, stands out for its lightweight design, portability, integrability with other sensing platforms, and notably low cost. These characteristics make it especially attractive for ecological monitoring, enabling high-resolution structural data collection even in projects with limited resources. Despite this potential, very few studies have evaluated the device’s performance or its capacity to support ecosystem characterization.

This research presents a comprehensive review and experimental assessment of the Mandeye LiDAR system to determine its suitability for environmental applications. Field data are being collected in Mediterranean forest and riparian environments using three acquisition modes, on foot, bicycle, and kayak, to test how platform mobility and scanning geometry influence point cloud quality. The study evaluates point density, coverage, structural accuracy, and noise sensitivity while integrating ground-truth measurements and independent LiDAR references.

Preliminary findings show that the Mandeye performs robustly across diverse environments, with kayak-based acquisitions offering particularly detailed representations of the vegetation-water interface. Walking and cycling configurations provide efficient alternatives for forest structure assessment. Overall, the results demonstrate the value of handheld LiDAR as a flexible, accessible complement to conventional remote sensing methods. The project also aims to establish methodological guidelines for Mandeye deployment, contributing to the broader adoption and standardization of low-cost LiDAR tools in ecosystem monitoring.

3D Gaussian Splatting (3DGS) has achieved remarkable success in novel view synthesis with dense input views. However, its performance deteriorates rapidly in sparse-view scenarios, particularly for viewpoints distant from training cameras. This degradation stems from two fundamental limitations: sparse initial point clouds from limited input views and insufficient viewing angle constraints for robust optimization.

To address these challenges, we propose VISTA-GS (Virtual Image Synthesis and Training Augmentation), a novel framework that leverages Multi-View Stereo (MVS) reconstruction for point cloud densification and generates virtual training views through alpha-blending rendering of MVS-reconstructed dense colored point clouds. Unlike existing approaches relying on generative models or learned priors, our method exploits the geometric consistency inherent in MVS point clouds to create physically-grounded virtual views. By rendering dense point clouds from strategically positioned virtual camera viewpoints, we generate additional training images that preserve accurate geometric relationships while providing crucial angular constraints, effectively regularizing 3DGS training without synthesis-induced artifacts.

Our main contributions are twofold. First, we address sparse SfM initialization by employing MVS for dense point cloud generation with adaptive depth-weighted ellipsoid scaling. Second, we introduce a rendering-based virtual view generation strategy that creates geometrically consistent training images around original viewpoints using the same alpha blending principle as 3DGS. This approach enables robust reconstruction from minimal input views (3-12 images), substantially improving novel view synthesis performance while maintaining geometric fidelity that generative approaches often compromise.

The digitisation of cultural heritage objects is an important procedure to conserve, share and analyse artefacts from the past. Nowadays, it is common practice to digitise artefacts using DSLR cameras and Structure from Motion. For most objects, this is a suitable procedure, but in some cases, objects have narrow interiors which cannot be reached with common camera equipment. Our case study is a small kayak model (~ 1 x 0.1 x 0.15 m) from the 19th century with an interior that can only be documented through small openings (0.1 m radius). We developed a method using a modified webcam to safely digitise the interior of the kayak. By comparing three datasets of a test object, we describe advantages and disadvantages of the usage of integrated autofocus and colour balance of the webcam. Furthermore, we extended our approach for segmentation of 3D models to consider the interior and prepare the models for future analysis. There were no major differences between the models of the three datasets, and all of them could reduce the data gaps in the 3D model based on the DSLR images noticeably.

Three-dimensional (3D) modeling for the documentation, preservation, and management of cultural heritage is indispensable. To achieve this goal, a low-cost unmanned aerial vehicle (UAV) combined with the Structure from Motion (SfM) photogrammetric technique was utilized to build a 3D model and conduct surface crack measurements of cultural monuments. The results showed that, under simple conditions, non-specialists can easily generate accurate 3D models from UAV-acquired imagery. In this study, the statistical errors of checkpoints between 3D reconstruction and field measurements, expressed as total RMSE, ranged from 0.103 m to 0.848 m. However, the mean absolute errors of surface crack measurements between tape-based methods and 3D reconstruction ranged from 0.002 m to 0.099 m. Furthermore, UAV-SfM was applied to measure surface crack lengths on an inaccessible cultural monument. The findings demonstrated that employing the UAV-SfM photogrammetric technique for 3D reconstruction of cultural monuments is both feasible and reliable.

Satellite remote sensing has become a vital tool for monitoring environmental change and supporting disaster management, offering consistent and wide-area observations of the Earth’s surface. Combined with the rapid growth of Earth observation data, machine learning (ML) enables the detection of complex spatial patterns and improves the prediction of geohazards. One significant hazard is ground deformation caused by coal mining, which threatens infrastructure, ecosystems and local communities. This study presents an interpretable ML framework that integrates multi-source geospatial datasets with eXplainable Artificial Intelligence (XAI) techniques to map deformation susceptibility in open-pit coal mining regions. Beyond achieving high predictive performance, the approach reveals the key factors controlling ground instability, including proximity to mining operations and faults, groundwater variation and topographic conditions. The results supports enhanced monitoring strategies for reducing disaster risks in mining-affected areas.

Accurate three-dimensional (3D) documentation of underwater environments is essential for evaluating the structural integrity of submerged infrastructure such as dams, pipelines or offshore platforms, as well as for repair operations or monitoring sites affected by potential pollution hazards including underwater chemical or ammunition residues. Automatic 3D surveying plays a key role in fulfilling these tasks remotely with a spectrum of uncrewed systems, such as remotely operated (underwater) vehicles (ROV), autonomous underwater vehicles (AUV) or robots. Conventional underwater surveying methods, including high

resolution imaging sonars and laser-based techniques, often require expensive instrumentation. Advances in photogrammetry and Structure-from-Motion (SfM) techniques enable detailed

3D reconstructions from standard imagery. This study presents a comparative accuracy assessment of two imaging devices for underwater SfM-based 3D reconstruction, giving practical workflow recommendations for low-budget underwater inspection and survey tasks.

Accurate georeferencing is a fundamental requirement in UAV based

photogrammetry, directly influencing the spatial

precision, reliability, and analytical value of the derived 3D

models. However, achieving high

accuracy in areas such as rockslides or steep geological

formations presents considerable challenges, primarily due to

the difficulty or danger associated with placing conventional

Ground Control Points (GCPs) on-site. This

study introduces a novel hybrid methodology that leverages

laser pointer indication and total station surveying to establish

high-precision reference points that can be safely and

effectively integrated into UAV photogrammetric workflows.

The proposed approach aims to improve the absolute and

relative accuracy of photogrammetric models without the need

for physical GCP placement in inaccessible or hazardous areas.

By integrating computer vision, photogrammetry, UAV technology, and Extended Reality (XR) solutions, the presented innovative Mixed-Reality (MR) photogrammetry system enables real-time 3D visualization, interaction and measurement of realworld

environments. By eliminating the need for physical presence, the system enhances safety, efficiency and accuracy in tasks like assessing structural integrity, tracking construction progress, and observing environmental changes over time. At the

core of the system is a UAV equipped with a stereo camera rig and onboard processing capabilities. Operated on-site by an operator, the UAV captures high-resolution stereo imagery, which is processed in real time through a centralized Rest API running on cloud infrastructure. Experts located anywhere in the world connect to the system using VR headsets or a webbased application, gaining immersive access to a 3D stereoscopic view with full photogrammetric measurement functionality.

The system supports multi-user collaboration, enabling synchronized analysis and data sharing across different locations. This seamless integration of hardware and software components represents a significant advancement in real-time stereoscopic visualization.

Accurate building footprints and 3D models are nowadays essential for a wide range of urban applications, yet the generation of Level of Detail 2 (LOD2) models remains constrained by the availability of dense 3D data such as LiDAR or image matching products. While these sources provide high geometric accuracy, they are costly to acquire and update, creating a gap between data availability and the increasing demand for city-scale 3D modelling. Recent advances in deep learning enable monocular height estimation from aerial imagery, offering a potential alternative to traditional 3D data sources. However, integrated workflows that combine image-based inference with structured 3D reconstruction are still limited. This paper presents CityZen, a point cloud-free workflow for LOD2 building reconstruction from only RGB orthophotos. The proposed approach integrates monocular height estimation (evaluating DSMNet, HTC-DC-Net and TSE-Net), roof type classification and model-driven reconstruction within a unified pipeline. Building footprints are used as geometric constraints, while learned height and semantic cues guide the generation of consistent 3D structures. The proposed framework enables scalable and practical LOD2 city modelling using widely available aerial orthophotos, reducing dependency on costly 3D data acquisition.

Documenting complex heritage sites, such as the QH36 Egyptian rock-cut tomb and La Lobera cave (Iberian sanctuary), often faces severe time and logistical constraints (e.g., concurrent activity, limited access). This necessitates a methodology that ensures fast data acquisition while maintaining high geometric and radiometric quality.

This study proposes a data fusion methodology combining Terrestrial Laser Scanning (TLS) and Spherical Photogrammetry (SP). TLS is prioritized for rapid, high-accuracy geometry acquisition, while SP, using a pre-calibrated 360-degree multi-camera, is utilized primarily for detailed texture mapping and supporting geometry in occluded areas.

A key element of this approach is leveraging the TLS point cloud to extract Ground Control Points (GCPs) and Checkpoints (CPs) directly, significantly reducing the need for time-consuming total station surveying and greatly improving field work efficiency.

Results demonstrate that the methodology achieves the core objective:

• Speed: Static capture time is reduced to approximately 5 minutes per station (TLS), less in the case of static spherical photographs, and even less using SP with video.

• Accuracy: Geometric registration errors given by TLS are less than 0.5 cm.

• Efficiency: Texture acquisition is improved at least 6-fold compared to conventional photogrammetry.

This validated approach offers a viable, efficient, and reliable solution for the high-quality 3D documentation of geometrically complex and time-constrained cultural heritage scenes.

High-precision attitude measurement in rocket recovery is critical for reusable launch vehicles (RLVs) and aerospace sustainability, but existing technologies have key flaws. Inertial Measurement Units (IMUs) accumulate drift, misaligning control commands with actual states; high-precision gyroscopes are costly and hard to integrate; Visual-Inertial Fusion (VINS) is light-sensitive, failing in dynamic re-entry—all risking recovery failure.

To address this, a large-field binocular vision method is proposed via four stages. First, camera calibration uses Zhang’s method for intrinsic parameters (left/right reprojection errors: 0.056/0.066 px) and control-point stitching for extrinsics, solving the large-field coverage issue and achieving 33.42 mm 3D positioning error. Next, image preprocessing applies bilateral filtering for denoising, Roberts operator for edge extraction, morphological closing for contour continuity, and multi-threshold Canny fusion to suppress spurious edges, ensuring stable input. Then, total least squares fits the midline, and left/right camera plane intersection extracts the rocket’s spatial central axis, avoiding noise from point-by-point triangulation. Finally, phase correlation resolves roll ambiguity from cylindrical symmetry, and the spatial axis calculates pitch/yaw to build a Z-Y-X Tait-Bryan angle matrix for attitude determination.

Experiments on a 1:20 scale model (1 m long, 0.3 m diameter) used µs-synced high-speed cameras (6 m height, 3 m baseline). Results show roll/pitch/yaw RMSEs of 1.58°/1.54°/1.41°, with 93% mean absolute errors ≤±2°—outperforming ORB+PnP (2.11° roll RMSE), SGBM (2.50°), and Chamfer (3.00°). Ablation experiments confirm key modules’ necessity—removing line support score filtering raises roll RMSE to 1.85°—verifying robustness in dynamic re-entry.

This study presents a low-cost, non-contact stereo vision system for automated river water level monitoring, addressing the growing need for dense and scalable hydrological observation networks under increasing climate-driven flood risks. The proposed system uses paired consumer-grade cameras combined with deep learning–based image segmentation to estimate water levels without requiring physical reference markers or pre-existing 3D models.

Two processing strategies are evaluated: a standard stereo workflow and an enhanced approach incorporating semantic masking to exclude dynamic regions such as water and sky. Camera pose estimation is assessed using both global and epoch-based optimization methods. Results show that unmasked configurations provide more stable and robust camera pose estimates, while masking improves geometric accuracy but introduces temporal instability.

Water level estimates derived from stereo reconstruction demonstrate strong agreement with reference gauge data, achieving correlation coefficients between 0.70 and 0.77. Both approaches successfully capture overall hydrological trends, including flood dynamics, although accuracy decreases under high water levels and challenging imaging conditions. Masking introduces a systematic offset in absolute values but does not significantly improve correlation performance.

This study applies cloud control photogrammetry to time-series archived aerial photos to support urban governance in Beijing. Addressing challenges such as missing ground control points, heterogeneous coordinate references, and non-digitized aerial triangulation results, the proposed method leverages existing basic geographic products (e.g., DOM, DEM) as dense control sources, enabling automated aerial triangulation and 3D reconstruction without field control points. The workflow includes control source selection and organization, image preprocessing, cloud control point and tie point matching, block adjustment, and time-series product generation. Three experimental applications are presented: (1) reconstruction of river course changes in the Beijing Municipal Administrative Center using KH satellite images (1961–1974) and 1996 DOM, yielding time-series DOM products meeting 1:50,000 scale accuracy; (2) detection of illegal self-built building additions via DSM differencing from ADS80 images (2016–2017), identifying one-to-three-story structures; (3) 3D real-scene modeling of the Grand Canal’s Tonghui River section from 1975 film photos and 2015 control data, revealing 40 years of urban transformation. Results demonstrate that cloud control photogrammetry ensures spatiotemporal consistency and enables quantifiable, multi-temporal 3D analysis for urban change detection, illegal construction monitoring, and cultural heritage preservation.

Sidewalk width is an important indicator of pedestrian accessibility, comfort, and network quality, yet large-scale width data remain scarce in most cities. Existing approaches typically rely on costly field surveys, high-resolution overhead imagery, or simplified geometric assumptions that limit scalability or introduce systematic error. To address this gap, we present UrbanVGGT, a measurement pipeline for estimating metric sidewalk width from a single street-view image. The method combines semantic segmentation, feed-forward 3D reconstruction, adaptive ground-plane fitting, camera-height-based scale calibration, and directional width measurement on the recovered plane. On a ground-truth benchmark from Washington, D.C., UrbanVGGT achieves a mean absolute error of 0.252 m, with 95.5% of estimates within 0.50 m of the reference width. Ablation experiments show that metric scale calibration is the most critical component, and controlled comparisons with alternative geometry backbones support the effectiveness of the overall design. As a feasibility demonstration, we further apply the pipeline to three cities and generate SV-SideWidth, a prototype sidewalk-width dataset covering 527 OpenStreetMap street segments. The results indicate that street-view imagery can support scalable generation of candidate sidewalk-width attributes, while broader cross-city validation and local ground-truth auditing remain necessary before deployment as authoritative planning data.

During Sulla’s siege of Pompeii in 89 BC, Roman artillery projectiles struck the city’s fortified walls, leaving visible impact craters. The subsequent eruption in AD 79 buried the site, preserving both its architectural layout and the damaged wall surfaces, which were later excavated in the early 20th century. By analysing the visible damage found on the fortified walls of Pompeii, reverse engineering techniques were used to decipher the engineering principles behind Roman military technology. This study simulates the impact of metal projectiles on grey tuff to estimate the impact velocities and the energy required to cause the observed damage, providing insights into the destructive capabilities of Roman weapons. It develops material models and applies finite element analysis, including mesh convergence, velocity calibration, and angular impact studies for both ballista stones and darts to better understand impact mechanics and crater formation.

metal darts on the city walls, along with the simulation of forces and trajectories. Among the objectives is to verify the calculated data against experimental relationships developed in antiquity and applied to the detection of small pyramidal indentations.

This abstract presents a deep learning and classical computer vision framework for cross-view geolocalization using 360-degree multi-perspective view (PV) images and an offline global map. Recent studies on cross-view geolocalization typically rely on deep learning models to localize panoramic PV images by matching them with reference satellite imagery. However, such approaches face practical limitations in real-world deployments, due to their dependence on large-scale GPU resources and the need to store extensive satellite image datasets. To address these challenges, we propose BEV-LOC, a lightweight and real-time cross-view geolocalization method. BEV-LOC employs Bird’s Eye View (BEV) encoder that learns to transform 360-degree multi-PV images into a local high-definition (HD) BEV map. The localization is then performed using Intersection Over Union (IoU)-based template matching with an offline global map. Our architecture achieves real-time performance at 30 FPS without the need for high-end GPU hardware and delivers a high positioning accuracy of 1.2 meters.

Monitoring geometric integrity of risers and pipelines is critical in offshore oil & gas operations, where swell, collapse or torsion often manifest as diametral changes that must be detected safely and efficiently. Historically, this kind of inspection is made by industrial climb, a time-consuming, dangerous and costly operation. Increasing efforts are on remote riser inspection using drones, primarily aimed at qualitative assessment through visual analysis, as well as photogrammetry, which offers accurate inspection but requires many images, image acquisition network design and well-trained drone pilots. To overcome the limitations of a qualitative image inspection and the complexity of photogrammetry, we propose a simple, low-cost method to estimate the pipe diameter from a single image by projecting two laser points of known spacing, building a scale directly in the scene and correcting depth differences between the laser projection plane and the pipe silhouette plane. This work evaluates the proposed method in laboratory conditions for nominal and calibrated focal lengths, distances from 2 m to 10 m and four pipe diameters, demonstrating the improvement of remote pipe diameter measurement by modelling and compensating for this depth difference. The improvement becomes more evident for longer focal lengths, shorter distances, and larger pipe diameters. It has an important effect in minimizing errors, e.g., from 3.5% to less than 0.2% at a 2 m distance for a 165 mm diameter pipe. The next steps include the construction of a lightweight projector to be integrated into a drone camera gimbal.

This study evaluates the effects of hand-crafted and AI-driven feature extraction and matching approaches on 3D scene reconstruction. While hand-crafted methods remain widely adopted in structure-from-motion (SfM), their performance often deteriorates when repetitive or uniform textures occur across multiple images, leading to alignment failures and incomplete reconstructions due to insufficient or erroneous feature correspondences. Recent advances in artificial intelligence have introduced robust pipelines capable of addressing these challenges by improving feature detection and matching in texture-repetitive imagery. In this study, hand-crafted and AI-driven feature extraction and matching techniques are integrated and assessed on challenging datasets to examine their performance in SfM-based 3D reconstruction. Experimental results demonstrate that combining hand-crafted feature points with AI-driven matching significantly enhances the robustness and reconstruction success rate across diverse challenging scenarios. This hybrid approach offers a promising alternative for reliable SfM 3D reconstruction when dealing with images dominated by repetitive or uniform textures.

Automated railway inspection is critical for safety, but current deep learning models are limited by a "closed-world" assumption, failing to identify novel or rare assets without costly retraining. This review explores a transformative solution: Vision-Language Models (VLMs). We introduce the concept of "reasoning-powered detection," where a model’s linguistic intelligence is used to guide the identification process.

Accurate 3D building modeling at level of detail 2 (LoD2) is

fundamental for urban analysis, supporting applications such

as realistic city simulations, energy assessment, and infrastructure

planning. While cadastral data is often freely accessible in

many developed countries, existing publicly available 3D building

benchmarks are typically limited either in scale or in the

diversity of input modalities required for developing and evaluating

modern deep learning methods.

We present a new large-scale, open, instance-wise dataset for

LoD2 building modeling from aerial imagery and LiDAR.

Through rigorous processing and validation, it bridges the

gap between raw open geospatial data and structured research

benchmarks. Its modular design supports both single- and

multi-modal reconstruction workflows. The upcoming public

release aims to enable reproducible research in 3D urban modeling,

cross-modal learning, and digital-twin creation, advancing

automated, reliable city-scale 3D reconstruction.

Accurate building segmentation from high-resolution aerial imagery is essential for various urban applications such as digital twins, geographic information system, and flood risk modelling. However, conventional supervised deep learning approaches require large amounts of pixel-level annotations, which are costly and time-consuming to obtain for large remote sensing datasets. To address this limitation, self-supervised learning has recently emerged as an effective paradigm in order to learn visual representations from unlabeled data. In particular, masked autoencoders (MAE) have demonstrated strong performance by reconstructing masked image patches during pretraining. Nevertheless, conventional MAE frameworks rely on random masking strategies that do not consider the spatial structure and semantic importance of regions in high-resolution remote sensing imagery. In this study, we propose GeoRGMAE, a geospatially guided masked autoencoder for building segmentation. Unlike standard MAE, which rely on random masking, our approach leverages building footprint annotations available in the pretraining dataset to guide the masking process while preserving the original reconstruction objective. We introduce three masking strategies -core, balanced, and density-aware masking- that prioritize semantically relevant building regions under the varying urban densities. The core strategy focuses on building interiors, the balanced strategy distributes masking between buildings and background, and the density-aware adapts masking based on scene-level building density. Experiments on the Roof3D and WHU Building datasets demonstrate consistent, though modest, improvements over standard MAE pretraining, with the most effective masking strategy depending on dataset characteristics. These results indicate that incorporating geospatial priors into masked image modelling can improve representation learning for downstream building segmentation tasks.

Photovoltaic panels are becoming increasingly popular, and finding a suitable location for them quickly and automatically is a current and practical problem. In our experiment, we test whether a point cloud from dense multi-image matching can be useful for the automatic detection of the best locations for installing photovoltaic panels. We propose a methodology for processing and analyzing UAV point clouds, where the use of deep learning in combination with the CANUPO algorithm results in high roof recognition efficiency.Two classes were selected: roofs and non-roof objects. This made it possible to filter the detected roofs and remove erroneous objects. The resulting model detected buildings with an accuracy of approximately 80% and an effectiveness of 100% (there were no false detections). the following factors were taken into account in the insolation calculations: roof angles, roof slope exposure, changes in the angle of sunlight throughout the year, and atmospheric transmittance. The roof angles and exposure were determined using a Digital Surface Model (DSM) generated from multi-image UAV data. In our research, we took into account the average angle of incidence of sunlight throughout the year and at quarterly intervals.The use of DSM for roofs and the SVC algorithm combined with CANUPO made it possible to eliminate false detections and significantly increase the effectiveness of location detection. Research conducted for the entire year and quarters enabled the analysis of changes in roof insolation throughout the year, which is crucial when estimating the profitability of installing photovoltaic panels.

This study investigates the enrichment of existing building models using deep learning-based window detection from oblique aerial imagery acquired by a high-end multi-camera sensor system. While many cities maintain LOD2 building models at Level of Detail 2, higher levels of detail require the integration of facade elements such as windows. Three detection strategies are evaluated using 3D reference building models to assess accuracy and completeness. The test site is located in Vienna and consists of multiple large residential buildings with varying facade characteristics.

The evaluated methods include zero-shot object detection with Grounding DINO combined with Segment Anything Model 2, applied to both oblique images and facade orthophotos, as well as a SAM2-UNeXT network requiring minimal training. Results indicate that zero-shot detection on orthophotos achieves the best performance, with a precision of 0.95 and an F1 score of 0.85. In contrast, the SAM2-UNeXT approach shows lower precision and F1 scores but slightly higher recall.

The investigation shows that detection performance is influenced by facade viewing angles. Steeper viewing angles generally improve detection quality but increase susceptibility to occlusions, particularly in dense urban environments. The article concludes with a detailed outlook on future work, including the extension of the approach to more complex three-dimensional building structures.

Point-cloud-derived 2D projections enable generating unlimited virtual views for indoor scene analysis and dataset creation. However, projecting irregular 3D samples onto a dense image grid commonly produces void pixels due to sparsity, occlusions, and incomplete scan coverage. These projection-induced artifacts degrade the visual fidelity of rendered images and limit their usefulness in downstream image-based workflows. This study investigates void-filling strategies tailored to point-cloud-generated RGB projections and provides a comparative evaluation of three representative approaches: (i) K-nearest neighbor (KNN) interpolation with KD-Tree accelerated neighbor search, (ii) a rule-based neighborhood method (NNRule) that adapts filling behavior using local variability to preserve edges, and (iii) a mask-normalized Gaussian-weighted propagation method that diffuses valid color information into void regions. Experiments were conducted on multi-view perspective projections generated from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) Area 3, totalling 5,520 images. Restoration quality was assessed using standard pixel-level metrics such as MAE, RMSE, PSNR, and SSIM. Quantitative results show that Gaussian-weighted propagation achieved the best overall performance, followed by NNRule, while KNN performed weakest numerically. Qualitative comparisons further indicate that KNN produces the most visually realistic texture appearance, whereas diffusion-based filling is softened fine details. Finally, the study establishes a practical baseline that enables both academic researchers to advance point-cloud-to-image restoration without relying on paired RGB datasets and industrial practitioners to deploy light weight void-filling pipelines in real-world applications such as digital twins, indoor robotics, facility management, and augmented reality.

Accurate forest inventories are essential for sustainable forest management. Handheld personal laser scanning (H-PLS) enables efficient and flexible forest data acquisition. However, ensuring reliable point cloud quality in complex environments remains challenging. While Simultaneous Localization and Mapping (SLAM)-based H-PLS allows rapid data collection, trajectory drift and accumulated registration errors can reduce the accuracy of derived tree parameters and structural metrics. In contrast, Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK) positioning provides centimetre-level absolute accuracy and drift-free trajectories, although its application in forested environments is still emerging. This study evaluates the impact of RTK-GNSS integration on point cloud geometry compared to SLAM-based point clouds without GNSS across two Central European forest plots with contrasting canopy structures. Analyses focused on tree parameter accuracy, structural metrics based on quantitative structural models, point density and noise characteristics. To isolate the effect of GNSS integration, data from the RTK-GNSS enabled H-PLS device were additionally processed without GNSS information, and an open-trajectory scan without loop closure was included for comparison. Results show that RTK-GNSS improves point cloud consistency and especially enhances the estimation of volume- and branch-related metrics. In the dense canopy plot, RTK-GNSS information reduced mean errors in branch number (−6100 to −5369) and crown volume (−492.75 to −357.21 m³). However, overall performance in tree parameter estimation depends on point density. These findings highlight RTK-GNSS H-PLS as a promising approach for flexible and efficient forest data acquisition in inventory applications.

Mobile Laser Scanning (MLS) provides high-accuracy 3D point clouds essential for road infrastructure monitoring. However, multi-temporal MLS analysis is often limited by non-constant, spatially varying trajectory drift caused by GNSS outages and IMU inaccuracies. These misalignments can exceed the magnitude of the changes being monitored, such as pavement deformation, making accurate change detection challenging. This paper presents a fully automatic, semantically driven registration pipeline designed to correct spatially varying drift in directly georeferenced MLS data. The method first applies Principal Component Analysis (PCA) and intensity-based filtering to classify points into stable geometric categories, including flat horizontal surfaces, flat vertical structures, and linear vertical features. A correspondence-based filtering step removes dynamic objects and temporal changes to ensure that registration is driven by stable geometry. The core of the method is an adaptive piecewise registration strategy, where the reference point cloud is divided into sequential 1-meter patches. Each patch is assigned a local rigid transformation estimated using an adaptively expanding registration window guided by the availability of stable vertical features. A final smoothing step ensures spatial continuity between adjacent transformations. The method was evaluated on two MLS datasets collected one year apart along a 3 km road corridor using the FGI Roamer-R4DW system. Validation using 30 independent ground signals showed that the 3D RMSE improved from 3.38 cm to 1.54 cm, with vertical RMSE improving from 2.54 cm to 0.67 cm. The results demonstrate that the proposed approach enables centimeter-level alignment suitable for high-precision multi-temporal road monitoring and change detection applications.

This work addresses the challenge of accurate 3D reconstruction of complex scenes such as vegetation, transparent, or non-Lambertian surfaces, which often cause difficulties for traditional Multi-View Stereo (MVS) methods. This issue is particularly relevant in the field of Cultural Heritage (CH), where many objects and environments exhibit such characteristics. To overcome these limitations, the study proposes the use of the new MILo (Mesh-In-the-Loop Gaussian Splatting) approach (Guédon et al., 2025), comparing its results with conventional MVS techniques and Terrestrial Laser Scanner (TLS) data.

MILo builds upon the 3D Gaussian Splatting (3DGS) technique, introducing a differentiable mesh extraction during optimization of the Gaussian parameters. This enables gradient flow between the volumetric and surface representations, resulting in more accurate and lightweight meshes, suitable for downstream applications such as simulations or animations.

The study uses three datasets: a Tilia tomentosa tree (Strasbourg) for complex natural geometries, the winter garden of the Sarreguemines Museum for reflective surfaces, and woodcarvings from Kasepuhan Palace (Indonesia) for fine ornamental details. Preliminary results on the tree dataset show that MILo significantly improves reconstruction quality, preserving thin structures such as branches and leaves compared to traditional MVS methods.

The final analysis will include both qualitative and quantitative comparisons (RMSE, standard deviation, completeness, mesh complexity) against TLS data, to rigorously assess MILo’s performance across different geometric and material conditions.

This study proposes a framework for detecting changes in outdoor civil infrastructure using bi-temporal images and validates its effectiveness through experiments on real-world datasets. The proposed method performs change detection by comparing a 3D Gaussian Splatting (3DGS) model reconstructed from multi-view images acquired before changes occur with a single real image captured from a new observation viewpoint after changes. The processing pipeline consists of: (1) construction of the 3DGS model, (2) generation of an initial rendered image corresponding to the post-change real image, (3) feature matching between the rendered image and the real image followed by camera pose estimation, and (4) change detection. Experiments conducted on a sediment control dam and a bridge dataset demonstrate that the proposed method achieves a maximum Intersection over Union (IoU) of 0.82 for change detection. Furthermore, compared to a baseline method based on bi-temporal real image pairs, the proposed method improves IoU by up to 24 percentage points. The results also indicate that even under limited acquisition conditions after changes, accurate change detection can be achieved when the 3DGS reconstruction quality and pose estimation are sufficiently reliable.

Light detection and ranging or lidar technology has been widely applied across various spatial domains. To meet the needs for a detailed underwater survey, Fraunhofer IPM developed an underwater lidar, known as ULi. The system has been tested under controlled laboratory conditions. Nevertheless, Fraunhofer IPM claims sub-millimetre range precision in clean water. However, no empirical study has managed to address this aspect, as fieldwork in the Elbe River (Walter et al., 2025) did not manage to obtain suitable data due to its naturally high turbidity. The present study will evaluate the geometrical accuracy of ULi against terrestrial laser scanner (TLS) and photogrammetry. An acoustic Doppler current profiler (ADCP) was chosen as a measurement target on the field experiment due to its rigidity and high reflectivity, with the dimensions of the frame is 75 × 75 × 65 cm. The data sets were georeferenced to the WGS 84/UTM Zone 48S coordinate system using control point targets affixed to the ADCP frame and measured with a total station applying the intersection method. Subsequently, the geometric accuracy assessment was performed through statistical evaluations, including root mean square error analysis and 3D point cloud deviation comparison among ULi, TLS, and photogrammetry data sets. The 3D model derived from the ULi data will be assessed against models derived from TLS and photogrammetry through statistical analyses of length discrepancies and spatial deviations. Additionally, intensity, point density, linearity, planarity, and scattering analyses will be performed to evaluate how well the point cloud represents the geometric characteristics.

Coded targets are widely used in close-range photogrammetry and 3D laser scanning for automated referencing and registration. However, most fiducial systems are optimized for specific software environments, limiting interoperability across processing pipelines. This study presents a cross-platform coded target framework for multi-sensor 3D acquisition that combines geometric redundancy, binary encoding, and human-readable elements to enhance robustness and reproducibility. An open-source implementation (PGT-Toolkit) supports marker generation, detection, and standardized coordinate export. Performance was evaluated using a controlled laboratory framework with systematically varied viewing angles, distances, and illumination conditions. Experiments were conducted using DSLR-based photogrammetry and terrestrial laser scanning. Detection rate, centroid repeatability, reprojection error, and cross-platform coordinate consistency were assessed and compared with those of established fiducial systems. Results demonstrate stable detection under oblique viewing geometries and consistent coordinate estimation across both commercial and open-source software environments. Laboratory studies confirm that Human Readable Coded Targets (HRCT) provide reliable, accurate, and cross-platform compatibility for both photogrammetric and 3D laser scanning workflows, which remain to be verified by field studies. The proposed framework contributes a structured methodology for experimental validation of interoperable coded targets in multi-sensor 3D workflows.

Constructing high-fidelity 3D models for urban Digital Twins is challenging, particularly for thin, texture-less structures like power lines where traditional Multi-View Stereo (MVS) fails due to matching ambiguities. While recent Monocular Depth Foundation Models offer dense estimation, they lack absolute scale and often degrade when applied to large-scale aerial imagery. This paper proposes a hybrid depth estimation pipeline that synergizes the metric accuracy of MVS with the structural coherence of foundation models.

Our method follows a Coarse-to-Fine strategy. First, we generate a scale-aware initial depth map by injecting sparse MVS points into the "Depth Anything" model as geometric priors, compensating for the lack of absolute scale in monocular estimation. Subsequently, a structure-guided refinement stage employs edge-based contour grouping to rectify object boundaries and suppress noise. Experimental results demonstrate that our approach successfully reconstructs power lines as distinct, linear objects with absolute scale, effectively resolving the data voids inherent in MVS and the geometric distortions typical of monocular models. This research provides a robust workflow for enhancing the precision of urban 3D reconstruction.

Valpelline is an unspoilt Alpine valley located in the northernmost part of the Aosta Valley, on the border between Italy and Switzerland. It is the region’s longest valley, shaped by glaciers and rivers, with elevations ranging from about 900 m to over 4000 m at peaks such as Mont Gelé (3518 m) and Dent d’Hérens (4171 m).

Since 2020, the glaciers have been monitored by the GlacierLAB group (Politecnico di Torino) and ARPA Valle d’Aosta. Because of the valley’s steep, inaccessible terrain, biannual aerial photogrammetric surveys with a GNSS antenna, a low-accuracy IMU, and a PhaseOne iXM-RS150F camera (151 MP, 50 mm lens).

Due to a lack of synchronization between the camera and GNSS, Ground Control Points (GCPs) are needed for georeferencing. However, their configuration is often insufficient. Camera calibration certificates (2019, 2022) are crucial to correct image distortions; when unavailable, calibration is estimated using Agisoft Metashape and Structure-from-Motion methods, dividing known points into GCPs and Control Points to evaluate residuals.

High-altitude flights require correction for atmospheric refraction, which affects image geometry independently of optical distortion. Tests were carried out to estimate refraction errors (via Saastamoinen formulas) and to separate them from optical effects, enabling more accurate 3D models of Valpelline’s complex alpine environment.

To produce suitable 3D models for downstream tasks, point clouds are often triangulated to reconstruct a triangle mesh, which first requires estimating normal vectors that define the local surface orientation. Because normals are not directly measured during laser scanning, they are often estimated in postprocessing using two steps: (1) selecting a neighborhood around each point and fitting a local surface function, and (2) orienting the resulting normal to distinguish inside from outside. Larger local neighborhoods often yield more consistent normals by averaging the surface, but can smooth out sharp discontinuities.

For orientation, various methods attempt to estimate the inside versus outside direction. In watertight scans, orientation can be determined by locally triangulating the points and propagating consistent normal orientations along the connected triangles. For surface scans containing holes and occlusions, typical for airborne LiDAR, this is more challenging, and heuristics like Minimum-Spanning-Trees or global flips towards one major coordinate axis are often used.

We propose a learning-based approach to estimate surface normals in unordered point clouds from airborne LiDAR scanning. Across multiple datasets, our approach consistently reduces artifacts and improves the quality of reconstructed triangle meshes compared to baseline methods, while achieving significantly faster runtime

This study investigates the feasibility of using UAV-based photogrammetry for the accurate extraction of railway geometry parameters such as gauge, alignment, and cant. The research explores whether aerial image-based reconstruction can meet the high precision requirements traditionally achieved through terrestrial survey methods. A series of experimental flights were carried out to evaluate how flight configuration, image quality, and processing strategy influence measurement accuracy and reliability. The results provide insight into the potential and current limitations of UAV photogrammetry for rail infrastructure documentation and quality control. Overall, the study contributes to advancing automated, efficient, and safe methods for railway inspection and geometric parameter extraction.

Dynamic beach locations world-wide are monitored by so-called Argus camera systems. Their automatic image capturing results in large databases of coastal images acquired during different illumination conditions. We present a lightweight and efficient method to automatically extract meaningful sand and supporting classes from ∼ 1 million Argus images of the Sand Engine, The Netherlands, a nature-based solution for beach erosion of 2 by 1 km. The method consists of 2 neural networks. First, a ResNet18 model selects images of sufficient quality. The second network, a shallow multi-layered perceptron is fed by RGB, intensity and texture features and classifies pixels into 6 classes, Water, Foam and Vegetation on one hand, and Aeolian, Wet and Armoured Sand on the other hand. Initial results shows good agreement with human interpretation. Final results will be used to assess the multi-year morpho-dynamic evolution at the hour scale of the Sand Engine.

Vegetation mapping in alpine environments is essential for monitoring ecosystem dynamics and climate change impacts, yet remains challenging when using very high-resolution UAV imagery under limited labeled data. This study proposes a data centric, pixel-based classification framework for species-level vegetation mapping using multispectral UAV data acquired in an alpine study area. The approach prioritizes improving data representation rather than increasing model complexity. To address label scarcity, a feature-rich dataset was constructed by integrating spectral information, vegetation indices, and lightweight spatial descriptors to enhance class separability. Classification was performed using XGBoost, which is well suited for multispectral tabular data and robust under imbalanced conditions. The results show consistent classification performance across vegetation types and demonstrate the effectiveness of dataset enrichment under limited supervision, highlighting the importance of feature representation in data-scarce scenarios.

Pavement crack segmentation is an important task in road infrastructure inspection. However, the practical deployment of deep learning-based methods remains challenging because many high-performance models require substantial computational resources. This limitation is particularly critical in large-scale Mobile Mapping System (MMS)-based workflows, where large volumes of road surface imagery must be processed efficiently. In this study, a lightweight CNN–Mamba hybrid architecture is proposed for efficient crack segmentation as a deployment-oriented redesign of CT-CrackSeg. The proposed model replaces the original MobileViT-based global modelling modules with EfficientViM-inspired blocks based on hidden-state mixer-based state space duality (HSM-SSD), while preserving the overall encoder–decoder structure. In addition, the boundary enhancement branch is refined by introducing DCNv2-based deformable convolution. Experiments were conducted on the publicly available GAPs384 and CamCrack789 datasets. The results show that the proposed model maintains competitive topology-aware segmentation performance while substantially improving computational efficiency. Compared with CT-CrackSeg, the proposed method improves inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.31 to 3.92 FPS on CamCrack789. At the same time, peak memory consumption is reduced from 2827 MB to 355 MB, while the clDice score remains comparable, changing from 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789. These results indicate that the proposed architecture provides a favourable balance between segmentation quality and deployment efficiency, and is suitable for large-scale pavement inspection and related photogrammetric infrastructure monitoring applications.

Historic gardens are dynamic Cultural Heritage, shaped by seasonal cycles, vegetation growth, and continual maintenance, and require documentation methods capable of capturing change over time. This study presents a multi-sensor, multi-temporal workflow applied to Villa Burba, a seventeenth-century garden near Milan, Italy. Two surveys conducted in 2023 (leaf-on) and 2025 (leaf-off) combined UAV photogrammetry with mobile laser scanning (MLS) to maximize completeness under contrasting environmental conditions. Both datasets were processed independently, harmonized within WGS84 / UTM Zone 32N, and evaluated through point density analysis, deviation modelling, MLS loop-closure checks, and GCP residual evaluation.

Multi-temporal point clouds were analyzed in QGIS using PDAL-enabled tools. Cloud-to-cloud differencing and canopy height modelling revealed key transformations, including the drying of a water channel, the loss of a historic tree, and spatial shifts in vegetation structure. These digital findings were confirmed through field inspection. The workflow demonstrates a practical approach for monitoring dynamic heritage gardens and supporting long-term conservation and management through accurate, repeatable 3D survey data.

Robust feature descriptors are necessary for computer vision applications such as image matching, photogrammetric three-dimensional (3D) reconstructions, and simultaneous localisation and mapping (SLAM). While most state-of-the-art feature descriptors are invariant to image transformations (such as translation, rotation, and scale) the majority lack stability in tracking points over large 3D perspective transformations. One successful method to solving these large perspective changes is by simulating affine tilts on the latitude and longitude axes of an image. These simulated tilts create greater invariance to changes in 3D perspective. To demonstrate the widespread efficacy of this approach, this paper applies affine simulation to seven state-of-the-art descriptors in OpenCV and to two of the enhanced OpenCV descriptors in OpenMVG.

The robust evaluation of Visual Simultaneous Localization and Mapping (vSLAM) systems is fundamental to their development and deployment. However, this process is often constrained by the reliance on expensive and complex external infrastructure, such as laser trackers or motion capture systems, to provide accurate ground-truth trajectories. This paper introduces a novel and self-contained methodology for the high-fidelity evaluation of stereo vSLAM and stereo-inertial algorithms. Our approach leverages the very same image sequence used by the SLAM algorithm to generate a dense, globally optimized photogrammetric model. The proposed methodology comprises two fundamental steps, the first step consisted of validating photogrammetry as a ground truth method. For this purpose, the linear displacement measured by photogrammetry was compared with the displacement of a precision guide, which was benchmarked against a laser interferometer as the standard. Once the reference was validated, the second step assessed the performance of ORB-SLAM 3 on a free trajectory within a complex environment, by directly comparing the SLAM result to the trajectory generated by photogrammetry. The accuracy was then quantified using standard metrics, including Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The results validate our approach as an accessible, low-cost, and reliable alternative for benchmarking vSLAM systems, enabling rigorous performance analysis using only the data from the sensor suite under evaluation.

Accurate estimation of tree volume is essential for precision forestry and sustainable forest management. Traditional forest inventory methods rely on manual measurements of tree height and diameter, which are time-consuming and costly to conduct over large areas, and difficult to perform efficiently in dense forest stands. This study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets. While the study relies on harvester production data (Sweden) and field-measured tree stem profiles (Brazil), the framework is designed to support the estimation of tree volume from close-range remote sensing techniques, such as terrestrial photogrammetry using handheld cameras. Three modelling approaches were evaluated, including two machine learning models (XGBoost and Random Forest) using partial tree stem profile measurements as predictors, and one baseline model (XGBoost) using diameter at breast height and tree height as predictors. The models were developed using two independent datasets: harvester production data of Norway spruce (Picea abies (L.) H. Karst.) from Sweden and field-measured tree stem profiles of Slash pine (Pinus elliottii Engelm.) and Loblolly pine (Pinus taeda L.) plantations from Brazil. The results show that tree volume can be predicted with reasonable accuracy using partial tree stem profiles, although models incorporating tree height achieved the lowest prediction errors. The findings demonstrate that partial tree stem profiles provide valuable structural information for machine learning-based tree volume estimation. This framework supports the future integration of close-range remote sensing techniques into modern forest inventory systems.

Semantic segmentation of Airborne Laser Scanning (ALS) point clouds is critical for numerous photogrammetric and remote-sensing applications. While deep learning has become the dominant approach for ALS semantic segmentation, most existing methods rely on predefined label sets and thus lack the ability to recognize arbitrary semantic categories. With recent advances in visual foundation models (VFM), zero-shot visual understanding has achieved notable progress in natural image domains. However, the potential of adapting 2D VFMs to 3D ALS point cloud segmentation remains underexplored.

This contribution develops three VFM-based approaches for zero-shot, open-vocabulary ALS semantic segmentation: Grounding DINO+SAM, CLIP+SAM, and GSNET. Grounding DINO+SAM identifies object regions using text prompts and employs SAM to refine segmentation masks. SAM+CLIP first generates instance masks via SAM and then assigns semantic labels using CLIP text and visual embedding. GSNET integrates a remote-sensing-specific encoder with a CLIP-aligned encoder to alleviate the domain gap between natural and aerial imagery.

Empirical study conducted on the ISPRS Vaihingen dataset demonstrate that all three methods possess certain zero-shot open-vocabulary capabilities. Methods trained solely on natural images perform well on common classes (e.g., roof, tree) but struggle with rare categories such as powerline. GSNET improves performance across most categories, highlighting the importance of domain adaptation; however, rare-class segmentation remains challenging. These findings suggest that substantial domain gap and limited representation of rare classes are key obstacles to applying VFM in remote sensing. Future research should focus on test-time adaptation and unsupervised domain adaptation to enhance VFM generalization for 3D ALS point cloud.

A workflow for the automatic extraction of the outlines of debris-covered glaciers and rock glaciers is presented. As the outlines in these scenarios are not clearly discernible, our approach is based on identifying geomorphological changes in multi-temporal 3D point clouds. We assume that these changes are caused by changes of the glacier. Consequently, areas with significant changes can be used to map the outline of the glacier. Our workflow uses pairs of multi-temporal 3D point clouds, which are captured for example by UAV imagery and TLS. After applying a robust registration algorithm, the difference of both point clouds is calculated. Considering only the areas that show significant changes, the glacier areas are isolated, and the outlines are mapped in a 2D mapping plane.

For evaluation, we test our workflow on two data sets. The Bøverbreen glacier, with only little debris cover, allows for a manual assessment of the glacier margins using an orthophoto mosaic from UAV imagery. A comparison of our calculated glacier margins with the manually assessed ones shows good agreement. The results confirm the basic functionality of our proposed method. However, tests show that the most challenging task is filtering glacial and non-glacial points, which is currently done solely based on the point density. More robust solutions to this problem will be discussed.

As large-scale infrastructure gradually ages, hundreds of existing bridges require regular inspections to ensure structural safety. While many researchers have proposed deterioration detection methods based on computer vision and deep learning—which can detect deterioration at the image level—no effective approach has yet been developed that integrates 3D reconstruction technology to achieve spatial localization and area quantification. To address this, this study proposes a two-part automated inspection workflow for the classification, localization, and measurement of internal deterioration in box-girder bridges. In the first part, the camera system is calibrated using an indoor calibration scene, and images are captured inside the box girder. A 3D model is constructed using Structure from Motion (SfM) algorithms, and a cylindrical projection unfolded map is generated. In the second part, a boundary-aware model—modified from DeepV3+—is used to perform pixel-level deterioration detection and classification on the unfolded map. Experimental results demonstrate that the system can generate scale-corrected cylindrical unfolded maps from 3D models with sub-millimeter scale accuracy (0.105 mm), effectively transforming complex 3D inspection tasks into measurable and analyzable 2D images. The model achieved an overall mean Intersection over Union (mIoU) of 65.11% across four categories of deterioration, representing a 7.54 percentage point improvement over the original DeepV3+. The research results validate the effectiveness of the proposed workflow in enhancing detection efficiency and objectivity for box-girder bridge maintenance.

In response to Hong Kong's smart city development strategy, this paper takes the 3D digital map construction project in Kowloon as a practical case study and systematically presents a construction method -for 3D digital mapping based on multi-source data fusion. Aiming at the technical challenges in high-density urban environments—including dense buildings, complex 3D traffic networks, and severe shadow occlusion—an "air-ground fusion" data acquisition strategy is proposed. By comprehensively adopting multiple approaches such as oblique aerial photography, Vehicle Mobile Mapping System (VMMS), and Portable Mobile Mapping Survey (PMMS), a high-precision and highly realistic urban 3D model has been constructed. The paper focuses on the principles of multi-source data fusion based on feature registration and combined adjustment, as well as the 3D modeling process and the quality control methods for the final results. The project’s technical innovation and practical feasibility have been validated through international benchmarking. The research results have been applied to urban planning, traffic management, environmental studies and other fields, providing a solid data foundation and technical support for Hong Kong's smart city development.

In recent years, the demand for 3D city model development has grown, as demonstrated by initiatives such as Project PLATEAU in Japan. In the construction of LoD2 building models, which are an essential component of 3D city models, the reconstruction of 3D roof models still heavily depends on manual work. To enhance productivity through automation, this study proposes a novel method for automatically reconstructing high-accuracy 3D roof models using orthophotos and Digital Surface Models (DSMs) derived from aerial imagery. In the proposed method, a deep-learning-based model is first applied to orthophotos and DSMs to extract 2D rooflines. Then, the extracted 2D rooflines are refined and polygonised to assemble 2D roof models. Finally, planar fitting was performed on the point cloud generated from the DSM within each 2D roof plane to reconstruct 3D roof models. In this process, the horizontal alignment of rooflines and the continuity between adjacent roof planes were preserved. In the experiments, 3D roof models manually digitized by stereoscopic measurement were used as the ground truth, and the automatically reconstructed 3D roof models were evaluated by comparison with this reference. As a result, the recall values for 2D and 3D roof planes were 0.686 and 0.430, respectively, and increased to 0.723 and 0.455 for roof planes larger than 4 m².

Neural scene representations are increasingly explored as alternatives to classical SfM and MVS in civil and architectural mapping, yet their ability to satisfy survey-grade geometric tolerances remains contested. This contribution examines how LiDAR guidance may stabilize NeRF and 3D Gaussian Splatting reconstructions of building façades obtained from low-cost cameras.

The Dunhuang murals, a significant component of China's cultural heritage, are severely threatened by salt-induced deterioration. To address the limitations of traditional invasive detection methods, this study explores a non-destructive approach using hyperspectral remote sensing to monitor mural salinity. Focusing on phosphate content, a key salt damage indicator, we propose a multi-level optimization framework that integrates Fractional Order Differentiation (FOD) for spectral enhancement and various feature selection strategies (including LASSO, SiPLS, SPA, CARS, and Random Frog) to improve prediction accuracy. Partial Least Squares Regression (PLSR) models were constructed using optimized spectral features. Results demonstrate that FOD effectively amplifies subtle spectral responses related to salinity. The model combining 1.9-order FOD spectra with LASSO feature selection achieved the highest performance, with a cross-validated R² of 0.908—a 15.96% improvement over the best model using FOD-transformed spectra alone. This study confirms that integrating FOD with advanced feature selection significantly enhances the precision and reliability of hyperspectral inversion models for mural salt damage, providing a powerful, non-destructive tool for cultural heritage conservation.

This work explores a new evaluation framework for 3D Model Quality Assessment using 3D intervisibility, a critical concept in 3D spatial analysis. In this work we will consider a high-quality LiDAR ground-truth 3D model and lower quality (dense matching and decimated) versions of it. Then we run the same intervisibility analysis on all of them and compare the results. This will allow us to evaluate the impact of geometric quality on intervisibility analysis This analysis is useful for anyone using 3D data for simulations, as it indicates what data quality they actually need to purchase or produce for their specific use case. Ultimately, the goal of this

work is to see how much the quality of the 3D model affects intervisibility results.

Recent adaptations of Neural Radiance Fields (NeRF) to remote sensing have shown strong potential for high-fidelity surface reconstruction from multi-view satellite imagery. NeRF represents a scene using multilayer perceptrons and optimizes a volumetric rendering objective to infer geometry and appearance. However, its performance declines sharply with the limited number of satellite viewpoints, and remote sensing imagery violates the simple reflection assumptions of natural scenes. Surface reflectance depends on material properties and illumination geometry, requiring explicit Bidirectional Reflectance Distribution Function (BRDF) modeling. In this work, a physically based NeRF formulation is proposed using the Hapke radiative transfer model, which efficiently describes surface–radiance interactions with a small set of parameters. This physically grounded approach is compared experimentally with empirical BRDF models, demonstrating its potential to enhance the physical realism and interpretability of NeRF reconstructions for Earth observation applications.

In this study, we built a mobile multi-camera system and investigated its use for photogrammetric 3D measurement of road surface geometry. More specifically, we tested the effect of driving speed on the quality of the 3D point cloud geometry on road surface. Our conclusion was that, with a five-camera system at speeds of 3-20 km/h, we achieved 3D distance errors of less than 0.5 mm when the data was compared to reference data measured from road surface samples. The results show that the method has great potential for producing sub-millimetre resolution and precision data on road surface damages, road roughness, and other road parameters. The

purpose is to use the system to collect reference data for verifying data from operational mobile laser scanning systems. The system can also be installed on other platforms and applications.

This research work presents the digital documentation of rock art found on a rock face in the Santa Olaya Canyon, in the municipality of Burgos, Tamaulipas. Unlike rock art found in caves, these open-air expressions are actively integrated with the natural and cultural landscape, functioning as symbolic markers of the territory. Through controlled flights with a DJI Mavic Air 2 drone and 3D photoreconstruction techniques, a difficult-to-access vertical surface of a rock face with rock paintings was recorded with high precision. The methodology employed responds to the need for conservation and study of these sites, which lack institutional protection mechanisms from the INAH (National Institute of Archaeology and History) or, as in this case, conservation and cultural research studies. It also contextualizes the value of rock art in Tamaulipas, particularly in the San Carlos and San Nicolás mountain ranges, where some of the most significant collections in northeastern Mexico are found. The application of non-invasive digital technologies is positioned as an effective tool for the documentation, analysis, and dissemination of archaeological heritage, especially in remote and limited-access regions. The generated orthomosaic and point clouds provide the opportunity to create a digital legacy of the area.

This work presents a deep learning-based framework for semantic classification of Mobile Laser Scanning (MLS) point clouds using a 3D Sparse Convolutional Neural Network (SparseCNN). The proposed approach addresses challenges specific to MLS data, such as varying point density, high data volume, and diverse urban or highway environments. A two-stage, coarse-to-fine classification pipeline is designed to ensure both scalability and high resolution: the first stage performs scene-wide semantic labeling, while the second refines ground-surface features such as road markings, sidewalks, and curbstones at finer spatial resolution.

To enhance robustness, the model is trained with tailored data augmentations including geometric transformations, density dropout, artificial noise injection, and local patch swapping. In addition to geometric input, radiometric features such as reflectance and echo information are incorporated to improve object differentiation, especially for materials like traffic signs and painted road surfaces.

Two sets of models are trained for different acquisition wavelengths (905 nm and 1550 nm), to account for the impact of laser wavelength on reflectance responses. Classification results on urban and highway scenes demonstrate the effectiveness of the method across a variety of environments and sensor platforms.

Semantic segmentation of point clouds, a fundamental task in 3D scene understanding, faces two persistent challenges. First, it is difficult to efficiently extract discriminative features for complex and irregular surfaces; existing methods struggle with the trade-off between simple features, which are insufficient, and complex features, which are computationally expensive. Second, many deep learning models ignore the inherent spatial correlation of point cloud features during the training process, limiting segmentation accuracy. Optimizing the feature representation for complex surfaces while fully leveraging feature correlation is key to advancing segmentation performance.

To tackle these challenges, we propose the Multi-Scale Umbrella Feature model with Spatial Self-Attention (MUSF-SSA). This model introduces a novel Multi-Scale Umbrella Feature (MUSF) to efficiently represent irregular surfaces and integrates a spatial self-attention (SSA) mechanism in its backbone to explicitly learn the spatial correlation between features.

Through these improvements, while maintaining a low parameter count (1.088M), our model achieves 68.6% mIoU, 76.5% mAcc, and 90.4% OA on the S3DIS Area-5 test, a typical indoor point cloud dataset. Compared to the similar method RepSurf-U, this represents a gain of +3.6% mIoU, +4.0% mAcc, and +2.6% OA.

The growing demand for clean and renewable energy sources has intensified the need to identify and exploit geothermal resources as a key solution for sustainable energy development. However, geothermal exploration faces significant challenges including geological complexity, high drilling costs, economic risks, and spatial data limitations. This study evaluates the efficiency of advanced machine learning algorithms, specifically Random Forest and Generative Adversarial Networks (GANs), in identifying geothermal energy potential areas in Akita and Iwate provinces, Japan. Using a limited dataset of 152 geothermal well locations, seven key parameters were analysed: volcanic activity, fault and fracture density, hot springs, surface thermal indices, fumaroles, mud volcanoes, and surface alteration evidence. Data were collected from geological and remote sensing sources and pre-processed for modelling. Results demonstrate that both algorithms effectively identify high-potential areas despite data scarcity. Random Forest achieved 94.08% accuracy in well identification with a C/S(C) index of 10.93, demonstrating robust performance and spatial correlation. The Generative Adversarial Network showed superior performance with 96.71% accuracy and a C/S(C) index of 4.36, indicating exceptional capability in identifying geothermal potential areas and detecting complex spatial patterns. These findings confirm that hybrid approaches combining machine learning and deep learning, particularly GANs, possess high capability for accurate geothermal prospectivity mapping and can effectively overcome limitations posed by data scarcity, providing valuable tools for exploration prioritization and investment decision-making

Building models are one of the key features in 3D city models. To realistically represent building exteriors, texture images are often applied to these models. Such textures are important not only for visual appearance but also for practical applications, such as automated generation of higher-Level-of-Detail (LoD) models and various urban simulations. In large-scale urban modeling projects, façade textures are typically obtained through aerial photogrammetry conducted by manned aircraft, primarily due to operational efficiency. In many such surveys, image acquisition is mainly based on nadir-oriented cameras. However, nadir-only imaging inherently limits façade resolution due to viewing geometry. In this study, we compare the façade resolution attainable from nadir and oblique cameras to examine the effectiveness of multi-directional camera systems in producing high-resolution façade textures. A theoretical approach is adopted to estimate the attainable façade resolution under given imaging conditions. A comparative analysis using the camera parameters of UCE M3 (nadir-only) and CM-2 (multi-directional) indicates several advantages of oblique cameras for façade texture generation: (1) significant improvement in the lowest façade resolution compared to nadir photography, (2) more consistent façade resolution across the entire survey area, and (3) limited sensitivity of façade resolution to increased camera station interval. These findings suggest that incorporating oblique cameras into an aerial survey system can contribute to stabilizing and enhancing attainable façade resolution compared to nadir only configurations.

Videogrammetry is widely used in fields such as structural health monitoring, surveillance, and aerospace, where accurate 3D measurements rely on precise calibration of stereo camera systems. Traditional planar target–based calibration provides high accuracy but becomes impractical for large-FOV setups due to the need for large, high-precision targets placed at long working distances. Control-field calibration, which uses spatially distributed artificial targets measured by total stations or GPS-RTK, similarly faces limitations in environments lacking accessible mounting locations. Other existing methods—such as rigid stereo-target calibration, close-range light-spot targets, and active phase targets—offer partial improvements but remain constrained by fabrication complexity, optimization instability, or limited depth-direction accuracy.

To address these challenges, this work proposes a flexible calibration method for large-FOV stereo videogrammetric systems using UAV trajectory imaging and epipolar geometry. A UAV carrying a rigid circular target flies through the measurement volume, while two synchronized cameras record its motion. Target centers are extracted using Circular-MarkNet, intrinsic parameters are obtained using an active-phase target, and scale-free extrinsic parameters are initialized from essential matrix estimation. The metric scale is introduced through static GPS measurements, and all parameters are refined via nonlinear optimization. Validation against a conventional circular-target control field shows that the proposed approach achieves comparable calibration accuracy within a 70–50–10 m volume while avoiding the need for large calibration targets.

The valorisation and dissemination of built heritage to the public is a crucial objective, complementing conservation efforts. However, traditional 3D models, such as dense meshes, often present limitations for this purpose, proving too heavy and complex for easy sharing and real-time visualisation.

This paper presents a hybrid approach that addresses this challenge by leveraging 3D Gaussian Splatting (3DGS) for the real-time visualisation of complex parametric models. This method is particularly effective for visualising 4D reconstructions representing historical phases of edifices that may no longer exist.

The methodology employs synthetic images generated from the parametric model using 3D rendering software. To ensure compatibility with procedural textures, path-tracing is used , but photorealistic effects such as cast shadows and reflections are deliberately removed. These optimised 3D renders are then processed through a conventional photogrammetric pipeline to generate the necessary camera orientations and sparse point cloud for 3DGS training.

The resulting 3DGS representation enables real-time rendering. This technique successfully converts a model composed of multiple, distinct parametric components into a single, unified object. This approach also demonstrates a strong capability for reconstructing contextual elements, such as vegetation, which are often poorly handled by traditional meshing techniques. The method effectively transforms a complex, software-specific model into a lightweight representation ideal for applications where visualisation speed is essential.

This study investigates the integration of photogrammetry and machine learning to enhance head pose estimation in radiation therapy. The primary objective is to improve the accuracy of patient positioning, which could reduce the reliance on immobilization masks, thereby enhancing patient comfort. The methodology involves the use of markers and cameras to track head movements, combined with machine learning algorithms to refine pose estimation. By merging deterministic photogrammetric techniques with advanced machine learning models, this approach aims to achieve more precise and reliable head pose estimation. The potential outcomes of this research could lead to more effective and comfortable radiation therapy treatments for patients with head-and-neck cancers.

This study aims to conduct a comparative analysis of individual

tree segmentation (ITS) methods for forest LiDAR point clouds.

Traditional ITS approaches have been predominantly based on

unsupervised segmentation algorithms using geometric features.

In recent years, research has progressively shifted toward super-

vised deep learning (DL) techniques. However, the perform-

ance of existing methods across diverse forest types has not yet

been systematically assessed.

Solving the exterior orientation of images is a fundamental component in photogrammetric mapping and 3D restitution processes. Additionally, it is essential in photogrammetric tasks such as visual odometry, camera-based visual simultaneous localization and mapping, camera calibration, camera-based 3D tracking of movement, and change detection. The aim of this research was to evaluate whether particle swarm optimization is suitable for finding the exterior orientation parameters of a single image using image resection. In addition, we developed a robustified particle swarm optimization by adding an iteratively changing stochastic model to the optimization criteria by attaching a weight matrix with residual vectors. The method was compared to the solution from the least squares method using both simulated ideal and noisy data. Solving the exterior orientation parameters reliably with particle swarm optimization was possible after fine-tuning the algorithm's options. The non-robustified version of particle swarm optimization provided identical results to the non-robustified least squares method. However, in the case of the robustified particle swarm optimization, only 60% of attempts resulted in the same outcome as the corresponding robustified least squares method, with sub-millimeter accuracy. In 40% of cases, the results achieved millimeter accuracy. The sub-millimeter accuracy was achieved in every case with sequential robustified particle swarm optimization, where the algorithm was rerun using stricter bounds for unknown parameters if the evaluation criteria were too large. The implementation of particle swarm optimization is easier than that of the nonlinear least squares method. However, the computation time for particle swarm optimization was significantly longer.

Geometric high-fidelity mesh reconstruction from LiDAR-inertial scans remains challenging in large, complex indoor environments– such as cultural buildings– where point cloud sparsity, geometric drift, and fixed fusion parameters produce holes, over smoothing, and spurious surfaces at structural boundaries. We propose a modular, incremental RGB + LiDAR pipeline that generates incremental semantics-aided high-quality meshes from indoor scans through scan frame-based direct label transfer. A vision foundation model labels each incoming RGB frame; labels are incrementally projected and fused onto a LiDAR-inertial odometry map; and an incremental semantics-aware Truncated Signed Distance Function (TSDF) fusion step produces the final mesh via marching cubes. This frame-level fusion strategy preserves the geometric fidelity of LiDAR while leveraging rich visual semantics to resolve geometric ambiguities at reconstruction boundaries caused by LiDAR point-cloud sparsity and geometric drift. We demonstrate that semantic guidance improves geometric reconstruction quality; quantitative evaluation is therefore performed using geometric metrics on the Oxford Spires dataset, while results from the NTU VIRAL dataset are analyzed qualitatively. The proposed method outperforms state-of-the-art geometric baselines ImMesh and Voxblox, demonstrating the benefit of semantics-aided fusion for geometric mesh quality. The resulting semantically labelled meshes are of value when reconstructing Universal Scene Description (USD) assets, offering a path from indoor LiDAR scanning to XR and digital modeling.

Map evaluation for occupancy grid mapping (OGM) is critical in the field of high-definition mapping of the road environment for autonomous vehicles. Existing methods cannot adequately evaluate the systematic and random errors that might be present in OGM. This article introduces two evaluation metrics for OGM under LiDAR position uncertainty: Mean Signed Distance (MSD) and Mean Absolute Deviation (MAD). MSD quantifies systematic displacement of occupied cells, while MAD measures random error exhibited as boundary thickening. Unlike classification-based, probabilistic, and geometric metrics, MSD and MAD directly isolate displacement and thickening effects in OGM. We validate both metrics in a controlled synthetic environment and on a real indoor LiDAR dataset, showing better performance than conventional metrics.

Deep learning techniques have demonstrated a promising efficacy for building feature extraction, presenting practical strategies to lessen the labour-intensive work of map updating, change detection, and urban growth monitoring. To address the labour-consuming challenges, a U-Net-based convolutional neural network model is developed to generate building maps automatically using high-resolution RGBI orthophoto and DSM data. The approach shows the effectiveness of the U-Net-based semantic segmentation for urban scene analysis. The presented procedures collect, preprocess, and combine orthophoto with DSM in order to train, apply, and assess the U-Net model for building extraction in urban environments using two input scenarios: (1) solely RGBI orthophoto and (2) RGBI orthophoto integrated with DSM. Four standard metrics: completeness, correctness, quality, and overall accuracy are applied to evaluate the model outputs, comparing the single orthophoto input to the combined orthophoto with DSM for building detection. The significant impact of the DSM and RGBI pairing is demonstrated by the heightened reliability of the data integration strategy when estimating buildings within nearby similar objects like roads and impervious surfaces. However, a few challenges related to the model's generalisation are noticed across complex urban contexts, including tree occlusions, unreferenced building extensions, and height irregularities surrounding structures. The findings highlight the potential of multimodal data fusion in urban investigations and reveal how it can improve the mapping of built-up assets. Final results argue that DSM incorporation significantly enhances building classification performance using deep learning frameworks for geospatial applications, particularly in complex urban environments where single data and traditional image-based segmentation methods face limitations.

The VRscan3D project introduces a virtual simulation environment for stationary and mobile laser scanning designed to enhance education, research, and AI-based point cloud analysis. Developed using Unreal Engine, the simulator replicates the physical behavior of real terrestrial laser scanners, allowing users to perform realistic scanning operations within immersive 3D environments. The system reproduces manufacturer-specific parameters such as range noise, beam divergence, and intensity, generating synthetic point clouds that closely approximate real data.

VRscan3D enables users to plan and execute virtual scanning campaigns, analyze data quality, and understand the influence of scanning geometry, surface materials, and user behavior. Recent developments include dynamic scene simulation with moving objects, integration of user-imported environments, and support for mobile scanning trajectories—handheld, vehicle-mounted, or UAV-based—reflecting natural oscillations and movement patterns.

In addition to training and education, VRscan3D serves as a generator of synthetic point clouds with known ground truth, facilitating the development and validation of AI algorithms for object detection, segmentation, and classification. Comparative studies between simulated and real scans demonstrate high similarity in terms of accuracy, resolution, and completeness.

By bridging real-world surveying practice and virtual learning, VRscan3D offers a cost-effective, accessible platform for universities and professionals lacking physical equipment or facing mobility restrictions. It represents a new step toward open, immersive, and intelligent learning environments in geospatial education and research.

Three-dimensional (3D) building models with accurate geometry and realistic textures remain essential for city information modeling and digital twin applications. However, photogrammetric reconstructions consistently suffer from severe texture defects caused by occlusions, shadows, distortions, and projection errors. Existing approaches either rely on rigorous photometric optimization that demands topological correctness and multi-view imagery, or employ flexible AI-driven generation that leverages semantics but often lacks geometric constraints.

This paper presents a novel hybrid framework that exploits architectural regularities—specifically massing decomposition and partial symmetries—to guide high-fidelity texture refinement. We first decompose building meshes into mass-aligned convex volumes using MorphCut. Textures are then reprojected onto these volumes, followed by Building Section Skeletons to pair symmetric facades and establish precise geometric correspondences. Finally, generative AI is applied using symmetry-aware constraints to achieve contextually accurate inpainting and correction.

Pilot studies on three Hong Kong buildings demonstrate robust decomposition, faithful texture transfer, and effective defect mitigation, while revealing current limitations of unconstrained generative models in preserving floor counts and structural regularity. The proposed symmetry-guided pipeline notably advances the reliable and semantically coherent reconstruction of textures for complex urban buildings.

The accurate digital documentation of Cultural Heritage (CH) assets demands workflows capable of integrating heterogeneous, multiscale datasets while preserving both geometric fidelity and radiometric completeness. This paper presents the first results of the AI-based processing pipeline developed within the HERITALISE project (Horizon Europe, 2025–2028), applied to three multiscale case studies at the Reggia di Venaria Reale (Turin, Italy): an outdoor-indoor UAV photogrammetric survey, a kinematic SLAM acquisition of a contemporary sculpture garden, and a close-range dataset of an 18th-century decorative artefact. 3D Gaussian Splatting (3DGS) is evaluated as a novel view synthesis method across all three scenarios, demonstrating strong photorealistic rendering capabilities, particularly for complex material properties and geometrically challenging interiors, whilst highlighting current limitations for metric surveying applications. A two-stage crack detection workflow, combining tile-based text-prompted segmentation with SAM3 and multiview ray-based reprojection onto the reconstructed mesh, is validated on UAV imagery, achieving an 84.9% ray–mesh intersection rate. Finally, a standardised evaluation framework is proposed, encompassing adaptive, scale-dependent geometric and radiometric metrics organised into reference-based and no-reference assessment scenarios, aggregated into a transparent synthetic quality score with three adaptive quality classes. The proposed methodology contributes toward a reproducible, sensor-agnostic standard for the assessment of AI-generated CH documentation products.

Haul roads are essential to open-pit mines, acting like the mine’s circulatory system. Keeping accurate, up-to-date maps of these roads is critical for maintenance, safety, and efficient material handling, yet automating this task is challenging. Traditional deep learning models that rely only on RGB images often fail in mining environments, where road surfaces resemble bare earth, dusty terrain, or shadowed areas. To address this, we propose a dual-encoder transformer that combines UAV-captured RGB images with DSM data using stage-wise cross-attention, leveraging both visual and topographic information. Two SegFormer encoders process each data type separately, creating detailed feature representations that are fused at each stage. This allows the model to learn specialized information while sharing knowledge between modalities. A lightweight All-MLP decoder produces the final segmentation map. We tested our method on a high-resolution dataset of 12,000 tiles from the Mildred Lake open-pit mine in Fort McMurray, Canada. Our model achieves 80.8% mIoU, 88.7% F1-score, and 73.7% road accuracy, outperforming an RGB-only baseline by 3.3%, 2.4%, and 7.8 points, respectively. Ablation studies demonstrate that including DSM data consistently improves recall and road detection, especially in areas where RGB information alone is ambiguous or terrain is complex.

This study presents a systematic benchmarking framework to evaluate the performance of local point cloud registration algorithms and their impact on geomorphological change detection. Three widely used methods—Iterative Closest Point (ICP), Point-to-Plane ICP, and Generalized ICP (GICP)—were tested across two alpine case studies in Italy (Rio Cucco catchment and Belvedere Glacier), considering different surface types and initial alignment conditions.

Three local registration methods—Iterative Closest Point (ICP), Generalized ICP (GICP), and Point-to-Plane ICP—were tested under varying initial alignment and terrain conditions using standardized voxelized patches (0.3 m). Performance was evaluated through median distance, cloud-to-cloud mean distance, and computation time metrics.

Results highlight the strong influence of surface morphology on algorithmic stability: rocky areas ensure reliable convergence, while dense vegetation introduces ambiguity and reduced accuracy. GICP provided the best compromise between robustness and efficiency.

The study further highlights that integrating robust outlier rejection significantly improves statistical consistency and reduces LoD95. The proposed approach provides a reproducible framework for optimizing co-registration strategies and improving the accuracy of geomorphological monitoring in high-relief environments.

Video human trajectory prediction is a fundamental research task for many applications in civil and defense. Compared to trajectory prediction based on a single frame, human trajectory prediction in videos, especially in the context of unmanned airborne vehicles (UAVs) platforms, is a challenge due to the time series prediction analyses required. As frames in a video streaming are highly correlated, trajectory detection in UAV images is affected by particular factors such as oblique camera views and the platform motion. This study aims to identify the most robust and accurate deep learning model in the context of UAVs videos by comparing three distinct categories: classical machine learning, established deep learning architectures, and computationally efficient models based on Multi-layer Perceptrons (MLPs). We propose an analysis based on only bounding box center coordinates instead of image scenes. The results show that a simple linear architecture provided the best performance, highlighting the importance of these mechanisms in predicting human motion from trajectory data alone.

This study presents a robust multi-technique methodology for generating complete, high-accuracy 3D documentation of highly constrained natural heritage sites, addressing the limitations of single-technique geomatic approaches. The research focuses on two challenging gorge environments in Southern Spain: Los Cañones de Río Frío and El Caminito del Rey. Both sites feature extreme vertical walls (up to 300 meters and narrow passages that complicate GNSS-RTK positioning and render individual UAV, TLS, or terrestrial photogrammetry techniques unfeasible due to occlusions and safety/logistical constraints. The proposed workflow centers on data fusion, leveraging LiDAR data for core geometry and photogrammetry for texture and gap-filling. Data acquisition integrated multiple sensors, including UAV LiDAR/Photogrammetry, Terrestrial Laser Scanning (TLS), Mobile Mapping Systems (MMS), and Spherical Photogrammetry (SP). A key methodological innovation involves deriving second-order Ground Control Points (GCPs) from UAV photogrammetry to georeference other data in areas with poor satellite coverage, significantly reducing fieldwork while maintaining accuracy. The highly precise TLS point cloud was used as the geometric base for the final model. The resulting products—including high-density point clouds and 2 cm orthoimages and 3D models—demonstrate comprehensive coverage and high accuracy (about 4 cm for georeferenced data), enabling 2.5D rockfall simulation and establishing a foundation for a Digital Twin of both gorges.

With the fast development of reality capture technology and the increasing availability and accessibility to devices capable of capturing 3D point clouds, a wide range of applications where cross-source Point Cloud Data (PCD) data interact appears to be more frequent. Augmented and Mixed Reality (AR/MR) technologies are pivotal for the integration between digital and physical environments by overlaying Digital Twin (DT) models into real contexts, and show themselves as capable of producing real-time 3D point cloud data. Nevertheless, the integration of AR/MR real-time 3D point cloud data with others such as LiDAR data still an open field for research specially at fundamental tasks such as scene alignment and camera localization. Conventional vision-based methods are vulnerable to environmental variations making achieving suitable camera localization and scene alignment challenging. Conventional vision-based methods are vulnerable to environmental variations, making achieving suitable camera localization and scene alignment challenging. This work proposes an exclusively 3D-o-3D-based methodology for AR/MR scene align alignment and camera localization addressing the challenges of cross- source point cloud registration in large size disparity scenarios. By combining cross-source point cloud registration via Voxel Representation and Hierarchical Correspondence Filtering (VRHCF) learning-based method TEASER++ algorithm, our approach effectively manages asymmetric heterogeneous point cloud data, achieving promising registration results especially in extensive indoor settings. The qualitative results suggest improvements over existing studies, despite outlier challenges in outdoor environments that warrant further research. This study highlights the potential and the essential need for advanced methodologies to enable seamless interactions between digital and physical worlds.

Indoor 3D scene reconstruction is essential for digital twins and intelligent spatial applications but remains challenging due to severe occlusions, weak textures, and complex geometric structures. This paper presents a semantic-guided high-fidelity indoor reconstruction framework based on 3D Gaussian Splatting (3DGS), which achieves high-precision geometry and photorealistic rendering through semantic-aware optimization. First, a high-quality geometric prior generation scheme is developed by integrating a 2D depth prediction network to enhance noisy depth data captured by mobile devices. The refined depth maps are processed by computing spatial gradients to derive surface normals in world coordinates, providing geometric supervision for the position and orientation of Gaussian ellipsoids. A projection-error-based filtering mechanism ensures consistency across multiple views. Second, a semantic-guided differentiated reconstruction framework is introduced. Using a pretrained segmentation model (SAM), the method distinguishes between large weak-texture areas and fine-detail regions. Normal regularization improves surface smoothness in planar regions, while detail-aware weighting strengthens local geometric fidelity. Additionally, a multi-view semantic consistency strategy jointly optimizes color and geometry across viewpoints, enhancing global coherence and reducing overfitting. Experiments on ScanNet++ and Mushroom datasets demonstrate that the proposed method surpasses state-of-the-art baselines in rendering quality and geometric accuracy. It effectively reconstructs continuous surfaces and detailed structures, showing strong potential for applications in virtual reality, digital twins, and real-time indoor modeling.

Shallow-water environments present significant challenges for underwater photogrammetry due to light caustics and the combined effects of absorption and scattering caused by water turbidty. These optical disturbances degrade image quality, disrupt feature matching, and ultimately reduce the reliability of 3D reconstruction using traditional SfM (Structure from Motion) pipeline. In this study, we focus on these two dominant factors and investigate a 3D reconstruction framework inspired by recent feed-forward architectures such as DUSt3R (Dense and Unconstrained Stereo 3D Reconstruction). To support this approach, we develop a synthetic data generation pipeline capable of simulating shallow-water visual conditions. Preliminary experiments indicate a possible trend for integrating physics-aware image formation with DUSt3R-type feed-forward reconstruction. However, several limitations remain: the current model does not yet achieve stable accuracy, real-world underwater validation has not been conducted, and computation costs remain high due to complex training procedures. Future work will focus on refining the network architecture, exploring DUSt3R-derived multi-view and high-fidelity extensions, accelerating computation, and validating the pipeline in real

shallow-water environments. Additionally, integrating advanced rendering techniques may further improvethe refinement of 3D reconstruction.

Digital Surface Models (DSMs) provide the fundamental elevation data required for generating 3D city models, which support a wide range of analyses such as solar potential estimation, urban heat island assessment, and infrastructure monitoring.

Advances in very high-resolution satellite stereo imaging, airborne LiDAR, and aerial photogrammetry have made it possible to generate DSMs at fine spatial resolution using different acquisition geometries and multi-view reconstruction techniques. However, these data sources differ substantially in terms of spatial resolution, viewing geometry, and surface visibility, leading to variations in elevation accuracy and morphological completeness. Airborne LiDAR surveys can provide highly detailed and accurate three-dimensional point clouds compared to aerial photogrammetry, but are associated with high acquisition and processing costs, as well as logistical constraints.

This study presents a comparative analysis of the DSMs derived from WV-3 panchromatic stereo imagery and oblique aerial photographs processed with the Structure-from-Motion (SfM) approach, focusing on the capability of SfM to reconstruct the complex urban morphology. The study area, a district of the city of Bologna, is characterized by a heterogeneous urban texture including compact mid-rise residential blocks, industrial facilities, vegetated zones, and open spaces, making it an ideal test site for comparing elevation models derived from different sensors and acquisition geometries.

Canopy Entropy (CE) quantifies 3-D forest heterogeneity from LiDAR, but its reliability depends on point density and kernel bandwidth. Using 11 sub-sampled airborne datasets (12–240 pts m⁻²) and bandwidths 0.1–2 m over a 20 ha Jiangxi plot, we show CE is stable (CV < 0.6 %) above 72 pts m⁻², whereas below 50 pts m⁻² it falsely inflates (> +5 %). CE grows logarithmically with bandwidth, saturating beyond 1 m; 0.2 m is optimal at landscape scale. Maintain ≥ 50 pts m⁻² and h ≈ 0.2 m for unbiased canopy-complexity mapping. An Investigation of the Application of GCE for Comparing Cross-Scale Structural Complexity Using Simulated Datasets.

Accurate registration of point clouds obtained from different viewpoints is essential for constructing consistent and reliable 3D models. Terrestrial laser scanner (TLS) data are typically represented in local coordinate systems centered at individual scanner positions, requiring transformation into a common reference frame. However, achieving high-accuracy registration for large-scale datasets remains challenging. Even small rotational errors in rigid transformations can result in significant positional deviations over long distances. Conventional registration methods, such as the Iterative Closest Point (ICP) algorithm, perform well in dense regions but often produce misalignments in sparse or geometrically uniform areas.

This study presents a high-precision point cloud registration approach that integrates global geometric features—such as planes and lines—with local point-based constraints. Plane and line features are extracted using RANSAC-based detection and incorporated into an enhanced ICP framework, improving both stability and convergence in large-scale environments.

Experimental evaluations using real TLS datasets acquired from an industrial factory demonstrate that the proposed hybrid ICP method significantly outperforms conventional approaches. The integration of global geometric features effectively reduces local misalignments and improves registration accuracy, particularly in regions with uneven point density or limited structural variation.

Automated 3D reconstruction technologies utilizing multi-source spatial data have gained significant attention in recent years. While conventional approaches rely on registration-based multi-sensor integration, recent Gaussian Splatting techniques have shown strong potential for large-scale modeling using only monocular imagery. However, existing 3DGS frameworks operate in relative coordinate systems and lack alignment with absolute geospatial references, limiting their applicability for real-world mapping.

To address these challenges, we propose a georeferenced Gaussian Splatting framework that integrates RTK-GPS camera position measurements directly into the training process. Initial camera parameters and sparse point clouds are estimated using an image-based SfM pipeline and subsequently aligned to a global coordinate frame through a similarity transformation based on RTK-GPS measurements acquired alongside the imagery. During coarse GS training, per-camera translation and rotation corrections are jointly optimized to compensate for geometric errors introduced during global frame alignment. The translation updates are guided toward RTK-GPS-measured positions, while a reprojection constraint based on SfM sparse 3D observations preserves the multi-view geometric consistency established by bundle adjustment.

The proposed method generates 3DGS outputs aligned with an absolute coordinate system with only marginal degradation in rendering metrics such as PSNR, SSIM, and LPIPS. Mesh conversion and surface-distance comparison with laser scanning data further validate the reliability of the reconstructed geometry. This work demonstrates the feasibility of real-world georeferenced modeling using Gaussian Splatting-based scene representation.

Digital twins of production facilities, represented as 3D virtual environments generated from point cloud data, are increasingly demanded for efficient facility management. Although terrestrial laser scanners (TLS) enable high-density 3D acquisition of such environments, the resulting point clouds are extremely large in data size. In practical applications, lightweight mesh models are therefore required as a substitute for raw point cloud data. However, TLS measurements often contain occlusions and missing regions, making it challenging to reconstruct complete mesh models directly from incomplete point clouds. Many objects installed in production facilities, such as equipment platforms, fences, columns, and ladders, consist mainly of planar surfaces. Efficient plane detection methods have been developed for large-scale point clouds (Masuda, 2015; Takeda, 2024). For objects composed of planes, 3D models can be reconstructed from the detected planes. However, industrial point clouds are extremely large, including many densely sampled planar regions. Furthermore, many existing methods focus on standard components with fixed shapes, such as pipe structures, and are not applicable to objects with more flexible geometries. To overcome these limitations, this study first converts the detected planar regions into simplified mesh representations to reduce data volume. We then construct a planar adjacency graph that preserves spatial relationships and geometric attributes between planes. Finally, we reconstruct the target structure by identifying and assembling appropriate subsets of planes.

Backpack mapping systems equipped with LiDAR sensors and RGB cameras, and an optional GNSS/INS direct georeferencing unit, are increasingly used in forest inventory applications. A key prerequisite to deriving accurate mapping products from these platforms is system calibration to establish the mounting parameters relating the LiDAR and camera sensors to the IMU body frame of the GNSS/INS unit. Conventional system calibration procedures entail specific trajectory and target deployment at the calibration site, followed by a labor-intensive identification of targets in imagery and LiDAR point cloud. Given the significance of multi-modal data alignment for forest inventory, this study explores an alternative approach for camera–LiDAR system calibration.

To address the limitations of existing RFM bias-compensation methods, which difficult to handle complex attitude jitter and lack fully automated processing, this study introduces an innovative Bundle Adjustment (BA) approach that incorporates adaptively determined spline smoothing parameters. The method constrains the smoothing term of the spline using prior matching accuracy and enables the adaptive estimation of the smoothing parameter within the BA process. Because the procedure requires no manual intervention and the adaptive smoothing term retains reasonable physical interpretation, the proposed approach is broadly applicable to the correction of attitude jitter in linear pushbroom satellite systems.

This work investigates the potential of optimizing underwater image acquisition while preserving reconstruction quality. A comparative evaluation of Multi-View Stereo (MVS) and Neural Radiance Fields (NeRF) is conducted, focusing on their performance in terms of completeness and robustness under conditions of reduced image availability. The study concentrates on underwater scenes involving Mediterranean coral species, where traditional photogrammetric methods often encounter difficulties due to occlusions and low-texture surfaces. The analysis is based on datasets acquired under controlled conditions, allowing for a direct comparison of the dense reconstruction capabilities of both approaches. The impact of decreasing the number of input images on reconstruction completeness and model accuracy is assessed, with results benchmarked against a reference dataset obtained using a triangulation laser scanner.

3D scene understanding is critical for applications like smart city management and urban planning. However, existing methods often treat 2D semantic understanding and 3D reconstruction as independent tasks, limiting the ability to create a unified 3D semantic representation. This separation hinders the accuracy, interpretability, and scalability of large-scale 3D scene understanding.

In this work, we propose a progressive, three-stage pipeline that seamlessly connects multi-view semantic understanding, self-supervised 3D reconstruction, and end-to-end semantic-level scene understanding. The approach gradually integrates semantic and geometric cues—first establishing reliable semantic priors, then recovering scene geometry without height supervision, and ultimately combining both into a unified 3D representation for more accurate scene understanding.

This paper introduces LIG-3DGS, a novel framework for robust 3D reconstruction and novel view synthesis under conditions where standard image-based methods struggle. The core of our approach lies in the deep integration of LiDAR geometry and intensity information with a 3D Gaussian Splatting (3DGS) representation. Our qualitative and quantitative experiments demonstrate that LIG-3DGS significantly outperforms standard 3DGS and geometry-only baseline methods under challenging photometric conditions. By bridging the geometric precision of active sensing with the high-fidelity rendering of neural approaches, this work opens a promising pathway toward all-weather, high-fidelity 3D scene understanding.

Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. LiDAR provides unprecedented detail of the 3D structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or at lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fitted line, and the DBSCAN algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars, planting densities, and growth stages were extracted and analyzed, and these performed well against the validation data. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.

This study presents a geometric framework for recovering missing CCTV camera parameters and deriving reliable three-dimensional viewshed coverage by matching UAV-based 3D mesh models with real CCTV imagery. Most CCTV metadata only contains approximate latitude and longitude, while essential calibration parameters such as azimuth, tilt angle, focal length, and field of view are unavailable. Without these parameters, visibility analysis in urban environments becomes inaccurate due to unaccounted building occlusions. To address this, a coarse-to-fine pipeline is proposed. In the coarse stage, camera tilt is estimated from the CCTV image using a monocular surface normal estimation model, and camera yaw is determined by matching cylindrical panoramic renderings of the mesh against the CCTV image using a dense feature matching network. In the fine stage, perspective projection images are rendered at 1 m height intervals using the estimated orientation, and each candidate is matched against the CCTV image to identify the optimal camera height. The rendering process simultaneously records world coordinates for every visible pixel, enabling direct extraction of 3D-2D ground control point correspondences from the best-matched candidate. Outlier correspondences are removed through Fundamental Matrix RANSAC, and spatially distributed representative points are selected via agglomerative clustering. Camera parameters are then estimated using an improved Perspective Projection Model with rotation matrix orthogonality constraints and weighted least squares adjustment. The recovered parameters are used to generate three-dimensional viewshed polygons. The method was tested on 41 CCTV cameras on a university campus and validated using reprojection error and ground-truth camera positions.

Determining the volume of unauthorised stockpiles or material deposits is a common task for environmental inspection authorities. Although UAV photogrammetry and laser scanning are widely adopted in many fields today, their use within environmental inspectorate practice may still be limited in some countries. In addition, archives of aerial imagery and laser scanning data maintained by national mapping agencies offer valuable resources for retrospective analyses of terrain changes caused by unauthorised material deposits; however, their potential has not yet been fully realised.

The objectives of this study are to: (1) present a comparative analysis of UAV photogrammetry and laser scanning in relation to terrestrial GNSS measurements for determining the volume of larger stockpiles and apply a model for volume accuracy assessment; and (2) demonstrate both the potential and limitations of using archived aerial imagery and laser scanning data for retrospective terrain-change analysis, with a focus on estimating the thickness and volume of deposits and their accuracy.

Both objectives stem from the current need for environmental inspectorate. Volume estimation can be highly sensitive because of associated penalties; therefore, understanding the accuracy and limitations of the applied methods is crucial. When time constraints are not an issue and dense vegetation poses a challenge (including grass cover that cannot be penetrated by laser signals), terrestrial GNSS or traditional surveying remain the most reliable options. Nevertheless, airborne photogrammetry and laser scanning offer undeniable advantages in terms of operability and retrospective analysis.

Airborne array InSAR achieves high-precision three-dimensional reconstruction through multi-baseline interferometric height measurement, holding significant application value in urban spatial structure monitoring and surface deformation analysis. However, the acquired urban InSAR point clouds are often affected by multiple factors, including platform attitude errors, system calibration inaccuracies, and multi-angle imaging geometric discrepancies, leading to noticeable spatial biases among different datasets. To achieve geometric consistency across multi-baseline data, high-accuracy point cloud registration has become a crucial step in InSAR data fusion processing. Therefore, the research proposed an improved ICP Algorithm Constrained by Intensity Gradient for Urban Airborne Array InSAR Point Cloud Registration. The improved ICP algorithm constrained by intensity gradients, which integrates geometric and electromagnetic scattering features. Experimental results demonstrate that the proposed method exhibits superior robustness and registration performance in complex urban scattering environments, providing effective technical support for 3D reconstruction of SAR point clouds.

Individual Tree Detection (ITD) is a primary step for estimating tree attributes such as spatial distribution, geometry, and species used in forest management, urban planning, and carbon accounting. While traditional field-based inventories are accurate, they are costly, labour-intensive, and limited in coverage. High-resolution UAV LiDAR offers a scalable alternative, and Deep learning (DL)-based object detection methods further enable automated ITD at large scales. In contrast to RGB imagery, UAV LiDAR can be transformed into multi-band representations that capture rich structural and textural information, which enhances ITD performance. However, previous methods still confront challenges presented by complex forest conditions, including overlapping crowns, and computational inefficiency when processing high-resolution, multi-band data. We propose AiDroneTree: a novel one-stage DL object-detection framework for multi-band rasterized UAV LiDAR, empowering more accurate and efficient tree detection in dense and heterogeneous forests to address this issue. The AiDroneTree architecture detects and segments the individual trees by combining a custom-built backbone and head optimized for detecting small trees in complex canopy environments with integration of Convolutional Blocks with Concatenate (CBC), LeakyReLU activations, and tunable layers throughout to detect bounding boxes and confidence scores for each tree. The results have been evaluated against YOLO on datasets captured from various environments with different tree shapes, sizes, and densities. The quantitative and qualitative results show that AiDroneTree outperforms YOLO in various forest conditions and achieves 91% accuracy, 93% precision, and 92% recall and F1-score.

The dam tailrace is a critical zone for dam safety, as high-energy spillway flows can deteriorate concrete slabs and drive scour along the downstream riverbed. However, this zone is difficult to access, and structural and geomorphic conditions are often assessed independently, limiting integrated understanding of their coupled behavior. Multibeam echo sounding (MBES) helps close this gap by providing high-resolution underwater topography and enabling simultaneous mapping of engineered concrete surfaces and erodible beds within a single survey. When deployed on unmanned surface vehicle (USV) platforms, MBES allows safe and efficient bathymetric mapping in narrow or high-energy downstream channels, supporting more complete characterization of tailrace conditions.

In this study, a USV-mounted MBES was used to acquire high-density underwater measurements across the tailrace of Daecheong Dam, capturing both the concrete stilling basin and the downstream alluvial bed. The resulting point cloud was segmented into two functional zones: (1) the concrete slab zone, where planar-deviation metrics quantified slab misalignment, elevation offsets, and localized deformations; and (2) the downstream zone, where terrain-based depression analysis delineated scour features and characterized their depth, extent, and morphology. By relating structural anomalies observed along the slab surface to the spatial distribution and severity of downstream scour, we perform a coupled slab–scour assessment that links block-level distress to localized erosion patterns near the apron-end transition.

This integrated approach demonstrates how MBES, combined with geospatial analysis, can support comprehensive underwater inspection and contribute to improved operational monitoring and hazard mitigation for large hydraulic structures.

This contribution presents a digital workflow for the virtual restoration and functional recovery of a historic xylographic matrix created by the 16th-century naturalist Ulisse Aldrovandi and preserved at Palazzo Poggi, University of Bologna. Although not physically broken, the pearwood block had undergone subtle yet significant geometric deformation over the centuries, preventing it from producing a complete and accurate print.

The project employed high-resolution structured-light scanning to generate a detailed 3D model of the engraved surface, capturing its geometry with sub-millimetric accuracy. From the resulting 31-million-polygon mesh, approximately 7000 points corresponding to the peaks of the engravings were manually extracted and interpolated to model the deformation. A corrective digital transformation was then applied directly to the mesh vertices, restoring the planarity originally required for printing without altering the object itself.

This case study demonstrates the potential of integrating high-resolution 3D surveying and digital modelling to address subtle geometric deterioration in historical artefacts. The method offers a fully non-invasive and reversible approach that can be extended to other wooden matrices or similarly sensitive cultural heritage objects. Future work includes testing additional surveying techniques and evaluating the reproducibility of the proposed workflow across a wider set of materials and conditions.

Modern AI applications increasingly rely on visual data for perception and decision-making, yet their reliability is fundamentally constrained by data quality and representativeness. Bridge inspection exemplifies this challenge: UAV imagery of bridge surfaces often exhibits complex textures, overlapping deterioration types, and severe class imbalance, limiting the performance of conventional deep models. To address these issues, this study proposes a data-centric approach within an integrated UAV-based bridge inspection framework. High-resolution UAV images are processed through photogrammetric calibration using Structure-from-Motion (SfM) and bundle adjustment, while a Swin-Unet segmentation model is trained with a data-centric sampling strategy that evaluates image patches through coverage, boundary, texture, and edge-entropy indicators to select representative samples. Experiments demonstrate that the proposed method achieves substantial improvements in mean IoU and F1-score compared with random cropping. The resulting multi-class deterioration maps are spatially integrated with 3D bridge models, forming a foundation for digital-twin-based inspection and confirming the effectiveness of data-centric optimization in enhancing the robustness of AI-driven infrastructure assessment.

Concrete dams require regular inspection because surface cracking and spalling can threaten durability and safety, yet UAV images of dam faces are often affected by low-light, blur, over-exposure, and stain-like discoloration that confuse automated crack segmentation. This contribution presents DamViT, a Vision Transformer–based framework for robust pixel-wise segmentation and 3D mapping of damage on concrete dams. UAV RGB images are annotated into three classes (background, crack, spalling) and used to train a SegFormer-based network equipped with two lightweight components: a degradation-aware module that estimates a per-pixel degradation map and guides feature extraction under low-quality imaging, and a stain-aware training strategy that explicitly balances stain-rich non-damage patches with damaged regions to reduce false positives on surface stains. The resulting three-class masks are back-projected onto a photogrammetrically reconstructed 3D dam mesh using camera poses and intrinsics, enabling computation of crack length, spalling area, and their spatial distribution in the structural coordinate system. The proposed pipeline links UAV imaging, robust segmentation, and quantitative 3D damage mapping to support dam safety management.

This study proposes an end-to-end automated pipeline for the generation, texturing, and semantic enhancement of 3D building models using UAV-based multi-source data, including imagery, image-derived point clouds, and orthophotos. The pipeline consists of three sequential stages: automatic 3D modeling, post-processing and texturing, and semantic integration. In the first stage, building candidates are automatically extracted from UAV-derived point clouds and orthophotos to generate geometric 3D models. The second stage refines the geometry through manual correction and applies texture mapping using UAV imagery and camera orientation parameters to enhance visual realism. In the third stage, façade images derived from building textures are processed through learning-based operators to detect semantic components such as windows. The detected 2D semantic information is converted into 3D coordinates and integrated into the textured 3D models, forming CityGML-like hierarchical structures within a .json framework. The resulting models contain both geometric and semantic information, offering high compatibility with CityGML and CityJSON standards. The proposed workflow demonstrates the potential for efficient, data-driven, and automated urban model generation that supports digital twin construction and spatial database updating. Future work will focus on incorporating LiDAR-based point clouds to further improve automation and semantic accuracy within the CityGML 3.0 framework.

This contribution investigates how caustic noise—bright, wave-induced light patterns—affects crack detection performance in shallow-water ROV imagery acquired at Daecheong Dam. Although many studies address underwater challenges such as turbidity, color attenuation, and motion blur, the optical distortions caused by caustic flicker have received little attention, despite being one of the most dominant artifacts in the 0–3 m depth range. Using real ROV video frames, we generated paired datasets with and without caustic-removal preprocessing and evaluated their impact on two lightweight CNN-based crack detection models (YOLOv5 and a transfer-learning AlexNet variant). Four filtering strategies were tested, including physics-based temporal median and motion-compensated averaging, as well as learning-based DeepCaustics and an FFT-residual method adapted from RecGS. Experimental results show that caustic-removal preprocessing consistently reduces false positives and improves crack visibility under diverse lighting conditions. The findings demonstrate that caustic noise is a critical but often overlooked source of detection instability in shallow-water inspections. The study emphasizes the importance of integrating simple, unsupervised caustic-mitigation steps into ROV-based monitoring pipelines to enhance the reliability of underwater infrastructure assessment.

Mobile Mapping Systems (MMS) provide dense point clouds essential for 3D mapping and infrastructure management, where semantic labeling is required to segment points into meaningful objects. Previous studies have shown that multiscale geometric features effectively capture local context for this task. Building on our previous work using multiscale features with efficient two-stage neighborhood search, we applied Contrastive Boundary Learning (CBL) to enhance classification accuracy near object boundaries. While CBL significantly improved boundary recognition, it also increased computational cost compared to Random Forest–based segmentation, limiting its practicality for large-scale datasets. In this study, we analyze the trade-off between segmentation accuracy and inference time in CBL-based boundary refinement. We further explore strategies to reduce computation while maintaining sufficient accuracy, aiming to achieve an optimal balance for practical MMS point cloud processing.

Ancient transport networks are central to studies of historical geography, regional socio-economic systems, and human mobility patterns. Traditional network reconstruction has relied primarily on the Least-Cost Path (LCP) model; however, the LCP’s “single-optimal” assumption is overly simplistic and cannot capture common historical realities such as the coexistence of multiple routes. Although probabilistic approaches such as Circuit Theory (CT) and behaviorally explicit methods such as Agent-Based Modeling (ABM) have been developed, a systematic, integrated framework that combines these approaches remains underdeveloped. Using the Yuncheng area of Shanxi Province as a case study, this paper systematically compares and integrates three distinct network models by constructing LCP, CT, and ABM networks and quantitatively comparing their differences in path morphology and predictive logic. The resulting multimodal, integrated probabilistic road network synthesizes the strengths of the three approaches and provides precise, high-confidence target areas for archaeological survey.

To protect and promote fish resources, fish habitat needs to be assessed and establish a “standard” for good or poor habitat for specific fish species. For this study, High resolution UAV images, including thermal image, are collected with an Anzu Raptor T for selected streams in East Texas. Orthomosaic and classification analysis were performed to make accurate map to represent open water, channel substrate and riparian vegetation. This approach provides a rapid means to assess streams. Future efforts will target finer geomorphic unit classifications (e.g., pool, riffle, run) across multiple river systems. This information can be critical for freshwater habitat management and restoration.

Road marking condition assessment is essential for transportation safety and road asset management, yet conventional inspection methods remain labor-intensive and inefficient. This study proposes an automated workflow for assessing road-marking conditions from drone imagery by combining object detection with a detector-guided segmentation strategy. First, road-marking regions are localized through a lightweight detector optimized for aerial viewpoints. The detected regions are then refined using a segmentation module that produces pixel-accurate masks, enabling reliable extraction of surface-level deterioration such as fading, cracking, and structural discontinuities.

The proposed approach was evaluated on drone datasets collected under varying flight altitudes and illumination conditions. Experimental results indicate that detector-guided segmentation significantly improves robustness to background clutter and enhances segmentation accuracy compared to single-stage models. The method also supports quantitative condition scoring, making it suitable for integration into municipal inspection workflows.

This contribution demonstrates the potential of combining detection and segmentation for large-scale, drone-based road-marking assessment, offering a practical solution for automated infrastructure monitoring.

Airborne laser scanning (LiDAR) technology has demonstrated exceptional capability in rapidly capturing dense point clouds and accurately representing complex surface features. It has been successfully applied across numerous geospatial and engineering disciplines with highly promising outcomes. The accuracy of any derived product inherently depends on the quality of the original LiDAR data and the processing methods employed. Therefore, evaluating data quality is an essential prerequisite for reliable analysis and application.

This study presents a quantitative assessment of LiDAR system performance, focusing on the intrinsic accuracy of the laser measurements themselves—an aspect often underexplored in existing literature. The evaluation was conducted through detailed field surveying using GPS triangulation and leveling techniques. Results reveal both planimetric and vertical accuracy characteristics, with a total elevation discrepancy of approximately 0.12 m and a horizontal RMSE near 0.50 m. The identified discrepancies exhibit two distinct components: a short-period random variation associated with the LiDAR ranging system, and a lower-frequency component influenced by biases in the geopositioning subsystem.

Airborne LiDAR offers high geometric accuracy and efficient wide-area coverage, and has been widely used in applications such as urban 3D reconstruction, forestry inventory, topographic mapping, and powerline extraction . However, due to near-nadir acquisition geometry and occlusions, vertical structures such as building façades are often under-sampled, resulting in large voids in the point cloud . Traditional geometric hole-filling methods, including Moving Least Squares, Poisson surface reconstruction, and mesh repair, are effective for small gaps, but they often suffer from over-smoothing, structural distortion, and topological discontinuities when applied to large-scale missing regions.

Meanwhile, multi-view imagery can recover continuous surfaces through dense matching or Gaussian Splatting, but the reconstruction quality still depends heavily on the completeness of the initial geometry. When the initial triangulated points or geometric priors are incomplete, façade regions remain prone to fragmentation and noise This paper proposes an image-assisted LiDAR completion framework that models LiDAR completion as continuous surface reconstruction with explicit Gaussians. Through anisotropic Gaussian initialization and tangent-plane-guided densification, the method preserves façade geometry and improves the completeness and accuracy of LiDAR-image fusion reconstruction.

Detecting objects hidden beneath a forest cover with Synthetic Aperture Radar (SAR) is challenging due to strong vegetation scattering, canopy attenuation, and ground returns. This work investigates two methods for detecting concealed targets using Polarimetric tomographic SAR (PolTomoSAR). The first approach exploits full-rank polarimetric tomographic focusing to achieve high-resolution separation of scattering sources and estimate their polarimetric responses. Target detection is then carried out using descriptors derived from decomposition techniques, such as the polarimetric entropy, and double-bounce scattering intensity, enabling the identification of man-made objects embedded within a dense vegetation layer. The second approach considers a compact configuration using only two interferometric SAR (InSAR) images. Coherent ground-notching suppresses the dominant ground scattering contribution, while preserving responses from above-ground scatterers. It is demonstrated that the baseline value plays a significant role in the detection process, and an optimum value is selected. Both methods are evaluated using L-band data set acquired by the DLR F-SAR over Dornstetten, Germany. Results demonstrate successful detection of concealed objects for varying baseline configurations.

This study aimed to develop a highly accurate crop classification framework using multi-temporal Landsat 9 imagery and advanced deep learning architectures for the Tokachi Plain, a major agricultural region in Japan. Six time-series scenes, acquired between May 2 and September 16, 2024, were used to classify six crop categories: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models with attention mechanisms were evaluated: long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN). Of the models tested, the TCN + Attention architecture achieved the highest overall accuracy (81.3%), significantly outperforming LSTM and Bi-GRU (p < 0.001). The Near-Infrared (NIR) band (Band 5) consistently exhibited the highest importance, highlighting its sensitivity to vegetation structure and chlorophyll content. Despite relying on only six optical scenes, the proposed model demonstrated robust performance comparable to or exceeding previous multi-sensor studies. These results underscore the potential of combining freely available Landsat 9 time-series data with attention-enhanced deep learning methods for efficient and scalable crop classification. The findings emphasize the important role of NIR reflectance during key growth stages and the effectiveness of TCN architectures in modeling temporal spectral variations for agricultural monitoring applications.

Optical satellite imagery plays a vital role in agricultural monitoring but is often constrained by cloud cover and illumination conditions. Synthetic aperture radar (SAR) offers an all-weather alternative, and recent advances in deep generative models provide opportunities to reconstruct optical-like imagery directly from SAR data. In this study, we investigated the potential of generating realistic red-green-blue (RGB) images of croplands using generative adversarial networks (GANs) trained on ALOS-2/PALSAR-2 quad-polarimetric (quad-pol) data. A distinctive feature of our work is the evaluation of not only backscatter coefficients (Gamma nought) but also polarimetric parameters derived from quad-pol decompositions, including the generalised Freeman–Durden, H/A/Alpha, and Yamaguchi four-component methods. Our results showed that paired image-to-image translation methods, such as feature-guiding GAN and pix2pixHD, achieved high similarity to PlanetScope reference imagery, with mean structural similarity index values exceeding 0.98 across all SAR inputs. In contrast, unpaired approaches demonstrated more variable performance depending on the input features. Notably, PUT showed significant improvement when H/A/Alpha or Yamaguchi decompositions were used, whereas Freeman–Durden produced results comparable to Gamma nought. The performance gap between paired and unpaired frameworks was most evident in heterogeneous landscapes, such as areas with adjacent grasslands and forests. These findings demonstrate the effectiveness of GAN-based translation from polarimetric SAR to RGB imagery for agricultural monitoring. The integration of polarimetric information adds value to unpaired learning schemes, and the ability to generate optical-like imagery under challenging observation conditions has strong potential for practical use in crop monitoring and assessment.

The performance of the Mask R-CNN model for instance segmentation of ceramic rooftops was evaluated using a high-resolution orthomosaic generated from UAV-based photogrammetry. Model training and inference were performed in ArcGIS Pro 3.5.3 with a ResNet-50 backbone. The model demonstrated high detection reliability, achieving a Precision of 96.62%, a Recall of 78.81%, and an F1-score of 86.81% at an Intersection over Union (IoU) threshold of 0.5. Most omission errors were associated with light-colored, elongated rooftops, highlighting limitations in the representativeness of the training sample and morphological variability. Fragmentation of larger rooftops into multiple segments was also observed, which affected accuracy metrics. To address this, a topological post-processing step was implemented to merge overlapping polygons, thereby improving segmentation consistency. These results indicate that Mask R-CNN is effective for high-resolution rooftop mapping, especially in applications requiring high precision. The approach is operationally feasible and transferable to similar datasets, enabling scalable analyses. It serves as a complementary tool for urban mapping, supporting the monitoring of urban dynamics and the analysis of construction patterns related to building standards and socioeconomic conditions.

Individual tree species classification from LiDAR (Light Detection And Ranging) point clouds has significant potential to support forest inventory and management, yet remains challenging due to complex three-dimensional canopy structures and the limited availability of labelled ground truth data. This study investigates self-supervised learning for tree species classification from LiDAR point clouds by comparing the PointMAE, a masked autoencoder-based model, with two supervised baselines, PointNet and PointNet++. Using the FOR-species20k dataset, two xperiments were conducted: a 33-species classification and a 6-species classification, each evaluated with point cloud sizes of 2048 and 8192 points. Using 2048 points, the PointMAE achieved the highest overall accuracy in both experiments (0.67 and 0.89 respectively), utperforming PointNet++ (0.63 and 0.84) and PointNet (0.39 and 0.75). Across all models, performance decreased when using 8192 points, indicating sensitivity to point cloud density

and sampling. Per-species analysis showed that coniferous species with distinctive crown geometries were the easiest to classify, while broadleaf species with similar crown forms, particularly Carpinus betulus, were the most challenging. These results show that self-supervised pretraining can improve classification accuracy over fully supervised approaches, highlighting its value for forestry applications where labelled data are limited.

This paper introduces the POD-HAR framework, a novel approach for deriving latent space dynamics in land surface modeling. The framework leverages Proper Orthogonal Decomposition (POD) to reduce data dimensionality by extracting dominant orthogonal modes and their temporal coefficients. It then applies Harmonic Analysis Regression with Sparsity (HAR) to identify sparse, interpretable nonlinear dynamical systems from this low-dimensional representation. By integrating these methods, POD-HAR establishes a regression-based technique for discovering parsimonious, often nonlinear, models that efficiently represent high-dimensional land surface evolution.

To address inefficiencies in manual inspection and color/geometric inconsistencies in tile production for web map services, this study develops an automated intelligent post-processing workflow. It integrates three core modules: automatic metadata quality inspection, computer vision-based image quality inspection (targeting invalid regions and color anomalies), and intelligent color uniformity adjustment with seamless edge fusion. By combining rule engines and image processing algorithms, automatic quality control and consistent fusion of produced/online tiles are achieved, significantly improving tile production automation and product reliability.

Classification of vessel types in Synthetic Aperture Radar (SAR) imagery is essential for maritime surveillance, yet distinguishing between ships with similar geometric characteristics—such as cargo and tanker vessels—remains challenging, particularly in medium-resolution images. This study investigates the role of wake patterns in improving ship-type classification using NovaSAR S-band imagery with 6 m spatial resolution. A dataset comprising 319 image patches (205 cargo, 114 tanker) was curated, including both centered ship patches and extended patches capturing wake structures. Experimental results demonstrate that incorporating wake information yields a 2–9% improvement across multiple evaluation metrics compared to ship-only scenarios. These findings highlight the potential of wake patterns as complementary features for enhancing classification accuracy in SAR-based maritime applications.

The growing demand for renewable energy has emphasized the importance of detailed geospatial information for photovoltaic (PV) site assessment and planning. Sentinel-2 imagery provides a valuable and widely accessible resource, yet its native 10-meter spatial resolution limits the ability to identify small structures such as rooftops, narrow roads, and compact built-up zones. This constraint affects the accuracy of solar suitability analyses and highlights the need for enhanced-resolution imagery capable of capturing finer spatial details.

This paper presents a photovoltaic (PV) assessment and optimization framework that integrates a resolution enhancement module based on latent diffusion models. This module operates in the latent space and relies on an iterative diffusion process to reconstruct fine urban and peri-urban structures, leading to higher-resolution products that support more accurate PV potential analysis and solar deployment.

Cloud-filtered Sentinel-2 L2A scenes are processed through this framework to produce ×4 enhanced imagery with an effective 2.5-meter resolution. Pretraining on

cross-sensor datasets can support realistic recovery of buildings, roads, and other small features while maintaining spectral coherence. The enhanced imagery enables more accurate rooftop segmentation, which serves as input for comprehensive photovoltaic potential assessment.

The installation optimization integrates multiple factors including solar radiation data, atmospheric conditions, shading analysis, rooftop orientation, tilt angles,

and panel layout efficiency to maximize energy generation capacity while considering technical and economic constraints. Qualitative evaluation demonstrates high-quality visual enhancement, confirming the relevance of this resolutionenhancement step within the overall workflow dedicated to PV site suitability analysis and installation optimization under real-world environmental conditions.

The given contribution describes an efficient artificial intelligence (AI) workflow for the detection and segmentation of ground-mounted photovoltaic (PV) systems in Bavaria (Germany), which can be transferred to any region. A two-stage approach was developed based on digital orthophotos (DOP) with a resolution of 20 cm (DOP20) or 100 cm (DOP100). Two different AI models, U-Net and YOLO, are used to identify and segment PV systems. The combined approach, which first analyses low-resolution DOP100 images and then uses targeted high-resolution DOP20 tiles, increases efficiency, by processing only relevant image areas with high resolution. Initial tests in three Bavarian districts show a high level of accuracy for both AI models. The approach is designed to be used for area-wide segmentation in Bavaria and thus contribute to change detection and quality assurance of the Digital Basic Landscape Model (ATKIS Base-DLM). Furthermore, the generalisation capability of the workflow was validated using an independent high-resolution dataset from the Piedmont region in Italy, where the models achieved promising recognition rates even without applying the post-processing pipeline.

We present a novel deep learning framework for above-ground biomass (AGB) estimation that produces high-resolution and multi-resolution biomass maps from coarse labels. The method is designed for the cases where dense pixel-level labels are unavailable. Using only 100 m scalar AGB values as supervision, our model predicts spatially detailed AGB maps at 100 m, 10 m, 3 m, and 1 m resolutions from PlanetScope imagery.

The task is formulated as a mass-conserving super-resolution problem, where each low-resolution label is reallocated over a high-resolution patch via learnable spatial weights. Our architecture is a lightweight encoder-decoder with four output heads, one per resolution scale. The final prediction is constrained to preserve total biomass per patch.

To guide spatial distribution without dense ground truth, we incorporate self-supervised learning (contrastive and equivariant losses), learnable pooling modules, and ecological priors such as NDVI/SAVI to suppress model hallucinations.

Trained on PlanetScope mosaics and ESA CCI-derived 100 m AGB maps, the model is evaluated on independent LiDAR-derived field plots. It explains 86% of the observed AGB variance (R² = 0.86) with only 2% bias, outperforming the baseline AGB map and recent CHM-based models in fine-scale detail.

This work demonstrates that both high-resolution and multi-resolution biomass mapping can be achieved from coarse supervision alone. It opens new opportunities for scalable AGB monitoring especially in data-scarce landscapes, with applications in ecological modeling, carbon stock estimation, and resolution-adaptive remote sensing.

Shadow correction is an important preprocessing step not only for visual enhancement but also for improving object recognition performance in remote sensing imagery. Although many datasets and deep learning models have been proposed for shadow detection and removal, most of them focus on natural images. In contrast, high-resolution aerial orthophotos contain large continuous shadows caused by tall buildings, especially in urban areas, and existing models often fail to handle such large-scale structures effectively.

In this study, we construct a new shadow annotation dataset specifically designed for aerial orthophotos with spatial resolutions of 20 cm/pixel and 5 cm/pixel. Furthermore, we propose a three-stage multi-resolution segmentation framework that progressively refines shadow predictions from low to high resolution. Predictions from lower-resolution stages are used as auxiliary information to guide higher-resolution prediction. Experimental results demonstrate that the proposed approach improves fuzzy Intersection over Union (IoU) by approximately 0.05 compared with a previously published shadow detection model, and also outperforms a single-stage baseline, particularly for large continuous shadow regions. The framework is also applicable to other large-scale segmentation tasks requiring extensive receptive fields.

With the in-depth penetration of UAV technology in fields such as geographic information surveying and mapping, the urban low-altitude economy has ushered in a critical period of rapid development. Among these fields, the safety issues in geographic surveying and mapping are particularly prominent. UAVs in this field are mainly used for field data collection of geographic information products such as digital orthophoto maps (DOM) and 3D oblique models. They realize fully automated flight mode through pre-set route planning, which significantly improves operational efficiency and operational convenience. However, they are confronted with the core technical challenge of "how to accurately determine the safety of flight routes within the survey area". This issue has become a key bottleneck restricting the safe and efficient operation of surveying and mapping UAVs.

This study takes remote sensing images and 3D geographic data as core supports, and combines multi-source data fusion technology and related algorithms to construct the "low-altitude flight safety field for urban surveying and mapping UAVs", drawing on the concept of “low-altitude safety corridors”. In essence, this field is a standardized digital 3D spatial grid system that covers the airworthy area of urban surveying and mapping UAVs, features three-dimensional connectivity, and supports intelligent coding. Shanghai was selected as a typical research area for data testing and verification. The test results show that the data achievements of this system can efficiently provide flight safety guarantees for the operation of surveying and mapping UAVs.

Precise monitoring of water resources is crucial for addressing global climate change. Water body extraction based on remote sensing imagery constitutes a core technical approach. Existing methods which based on CNN or Transformer (Chen et al., 2018; Gu et al., 2022; Lu et al., 2024), still encounter challenges when processing high-resolution imagery, including blurred boundaries, significant scale variations, and low computational efficiency. This makes it difficult to achieve a high degree of balance between accuracy and efficiency in water body extraction. To address these restrictions, this study proposes a residual network model integrating multi-scale contextual attention, called as MSCTFormer. It provides a novel approach for achieving high-precision and high-efficiency water extraction.

This paper proposes the MCAM model to address key challenges in hyperspectral image (HSI) classification. The core of the model comprises a cyclic adaptive scanning module, which achieves multi-view feature fusion through dynamic weights, and a multi-scale convolutional block, designed to extract hierarchical spatial features. Combined with an improved loss function, the model significantly enhances the discriminative capability for confusing land-cover categories. Experimental results on several public datasets demonstrate that MCAM outperforms existing methods in classification accuracy.

This paper proposes a modular fusion framework for delineating individual tree crowns from airborne LiDAR-derived canopy height models in a temperate mixed-wood forest in Ontario, Canada. Current instance segmentation models require expensive polygon annotations and tightly couple detection with segmentation, making cross-architecture fusion difficult. Limited forestry training data further causes transformer detectors to collapse on small datasets. The proposed framework decouples detection, fusion, and segmentation into independent stages. Two detectors, Faster R-CNN and DINO, are implemented with both ResNet-50 and domain-specific Masked Autoencoder backbones, with supplementary Finnish Taiga data stabilizing transformer training. A threshold-anchored score normalization maps each detector's confidence to a common scale before Weighted Box Fusion, enabling fair combination of architectures with incompatible confidence distributions. The fused bounding boxes prompt the Segment Anything Model (SAM) to generate per-tree polygon masks without domain-specific mask annotations. SAM's automatic mask generator additionally fills gaps where both detectors missed trees; SAM 1 is preferred over SAM 2, which produced fewer than half the automatic masks and missed smaller understory crowns. On two test plots with 233 and 107 ground truth trees, the framework achieves mask F1 scores of 0.79 and 0.61 at IoU thresholds of 0.25 and 0.50, matching 193 of 233 trees on the primary plot. Visual inspection indicates that many SAM-generated boundaries align more closely with canopy structure than the reference polygons. The modular design allows components to be independently replaced or upgraded, providing a practical pathway from LiDAR-derived CHMs to polygon-level crown delineation in data-limited forestry applications.

Remote sensing image captioning (RSIC) aims to describe key objects in remote sensing images using natural language, with significant applications in disaster assessment, land-use identification, and scene understanding. Existing methods face two critical challenges: insufficient cross-modal alignment due to the domain gap between generic visual representations and remote sensing semantics, and inadequate spatial relation modeling among regions in complex scenes, which compromises the semantic precision and logical coherence of generated descriptions. To address these issues, this paper proposes the Dual-Stream Relation-Aware Transformer (DSRAT) for remote sensing image captioning. On the visual encoding side, multi-scale CNN features serve as the foundation, fused with domain-specific semantic priors from RemoteCLIP through a gated dual-stream fusion module to achieve adaptive alignment of multi-source visual information. Subsequently, a spatial relation-aware mechanism is introduced into the encoder self-attention, which explicitly encodes geometric relationships such as relative position, distance, and orientation between regions as attention biases, enhancing the model’s capability for structured representation of complex spatial layouts and multi-object interaction scenarios. Finally, adaptive weighted aggregation of multi-layer encoder outputs generates discriminative cross-modal memory representations for the decoder. Experiments on the RSICD and NWPU-Captions datasets demonstrate that DSRAT achieves state-of-the-art performance across six metrics on RSICD and all seven metrics on NWPU-Captions. In particular, DSRAT achieves a significant performance improvement of +14.45 CIDEr on NWPU-Captions compared to the state-of-the-art method, validating the effectiveness of the proposed approach.

This study investigates a weighted ensemble framework for individual tree crown (ITC) delineation using LiDAR-derived canopy height models (CHMs). Three deep learning models, Mask R-CNN, U-Net, and YOLO were first independently evaluated to establish the baseline performance under consistent training and evaluation conditions. A weighted ensemble was then constructed by combining model outputs through a voting‑based fusion scheme, with an exhaustive search performed across multiple weight configurations to identify the ones that maximize common evaluation metrics. While certain weighting configurations yielded improvements in quantitative measures such as intersection over union (IoU), recall, F1 score, and accuracy relative to individual models, qualitative analysis revealed that these gains often coincided with substantial under segmentation, manifested as large, merged crown regions. This discrepancy highlights the limitations of binary map voting for instance level delineation and indicates that metric driven ensemble optimization may not reliably reflect instance level segmentation quality. The findings suggest that more expressive fusion strategies may be necessary for effective ensemble based ITC delineation in future work.

This paper presents an integrated approach using hyperspectral spectroscopy, granulometric analysis, and Sentinel-2 multispectral imagery for detailed mapping of carbonate beach sediments on Nogas Island, Philippines. By constructing a spectral library from field and laboratory data and employing the Spectral Angle Mapper (SAM) algorithm alongside the Grain Index, this study characterizes spatial variability in sediment grain size and carbonate composition. The methodology combines field sampling with remote sensing to generate maps that reveal sediment texture patterns influenced by hydrodynamics and depositional environments. The findings demonstrate that finer carbonate sediments exhibit higher reflectance and distinct spectral absorption features, enabling differentiation from coarser grains. This research highlights the potential of integrating multispectral satellite data with hyperspectral spectral libraries to provide rapid, reliable coastal sediment assessments critical for environmental monitoring, biodiversity conservation, and sustainable management of vulnerable tropical island beach systems.

For an increasing number of applications, land cover maps can be generated from remote sensing imagery using conventional and deep-learning-based semantic segmentation models.

Relying on a large pool of training data, the networks struggle with the spatial-temporal-spectral heterogeneity in the complex and diverse remote sensing imageries, leading to a significant number of errors in the model predictions.

This paper presents a workflow comprising domain adaptation and classification.

In particular, we analyze two domain adaptation techniques: First, a conventional histogram-matching method, which has turned out to be a surprisingly fast and reliable tool in a previous study, and second, a CycleGAN, which we applied both in its standard form and with the perceptual loss, thereby penalizing style inconsistencies on deeper layers.

By applying the workflow to three remote sensing datasets and six directions of domain adaptation, we show that there is ``no free lunch'' in the sense that all domain adaptation methods have their advantages.

Depending on the dataset, classification method, and especially on the availability of 3D data, the performance gap can be reduced to up to 1.5\% of the mean F1 score, demonstrating the soundness of the proposed method.

Road network is an important infrastructure of urban spatial structure and traffic system. Its accurate acquisition is of great significance for urban traffic analysis, automatic driving map construction and disaster emergency response. With the wide acquisition of high-resolution remote sensing images, automatic extraction of road masks from remote sensing images has become an important research direction in the field of remote sensing image understanding. However, the existing deep learning methods still face the problems of obvious modal differences and insufficient modeling of road structure continuity in remote sensing scenes. To solve the above problems, this paper proposes a remote sensing image road segmentation model LR-SAM based on SAM (Segment Anything Model). In this model, the LoRA (Low Rank Adaptation) fine-tuning strategy is introduced to achieve efficient parameter updating, and the MS multi-scale feature interaction module is designed in the coding phase to enhance the expression ability of the linear structure and fine-grained information of the road. At the same time, the original prompt encoder is removed and a lightweight ad decoder is constructed to achieve multi-scale feature fusion. In the reasoning stage, TTA (Test Time Augmentation) strategy is introduced to improve the stability and segmentation accuracy of the model. Experimental results based on chn6-cug and SAT-MTB datasets show that the proposed method achieves 97.20% and 85.06% mIoU and 96.67% and 84.94% F1-score, respectively, which is significantly better than the mainstream road segmentation method, and verifies the effectiveness of the proposed improvement points.

With the development of Earth observation technology, China's domestic high-resolution satellite remote sensing technique has achieved high-quality development. Currently, in orbit land resource satellites can obtain over 4500 images globally every day. With the explosive growth of data volume, traditional image processing method can not meet users demand for spatial information services with high frequency and large area. How to achieve automated image processing with massive data volume, and to provide real-time image services to users efficiently has become an urgent problem to be solved.

The recent volcanic unrest on Iceland's Reykjanes Peninsula was an excellent opportunity to better understand volcanic processes and develop hazard mitigation strategies. The eruption was studied using various direct and remote-sensing techniques. Here, we present an innovative UAV-based TomoSAR approach application, combined with photogrammetry, to explore the external and internal structures of an active volcanic vent within the Sundhnúkur crater row, where nine eruptions have occurred since December 2023. The surveys were conducted on 20 May 2024 (12 days after the end of the March–May eruption) and on 1 August 2024 (40 days after the May–June eruption). For optical data collection, we used a DJI Mavic 3T quadcopter, equipped with an RGB camera and an infrared sensor. The radar data were acquired using a UAV-based interferometric SAR system, Explorer RD350, which is capable of collecting P-band data in helical-trajectory mode. The optical data were processed using the standard photogrammetric workflow, and the SAR data were processed using the Refractive Back Projection algorithm, which enabled the extraction of amplitude images as slices at given depths with a ground penetration of up to 20 m. Our results show that the higher-intensity areas in the subsurface images correspond to the vent's crater center, while the lower-intensity areas correspond to the slopes of its cinder cone, composed of loose volcanic material. We assume that the higher-intensity areas in the amplitude images represent structures of denser material at depth, e.g., a lava conduit within the volcanic cone.

Analysis of the Phoenix area between 1992 to 2024, using DMSP-OLS and VIIRS Data.

Image super-resolution (SR) techniques have achieved significant performance improvements with the advancement of deep learning. Accordingly, deep learning-based SR methods have become the mainstream approach in SR research and are widely applied across various fields, including remote sensing. However, most state-of-the-art SR studies are primarily driven by computer vision research and tend to focus on generating visually realistic images rather than preserving structural fidelity with respect to the input images. In remote sensing applications, maintaining structural fidelity is particularly important because SR outputs are often used in downstream analytical tasks such as object detection.

In this study, we investigate the use of edge loss to enhance the structural fidelity of SR images for remote sensing imagery. The effectiveness of edge loss was evaluated using multiple benchmark datasets on both convolutional neural network (CNN)- and generative adversarial network (GAN)-based SR models. Several representative SR network architectures and GAN training frameworks were employed to assess the impact of integrating edge loss into the training objective. The experimental results demonstrate that incorporating edge loss improves both the structural fidelity and perceptual quality of SR images. Among the evaluated edge operators, the Prewitt-based edge loss showed the most consistent improvements compared with the Sobel- and Laplacian-based edge losses. These results indicate that edge loss is an effective and easily implementable strategy for improving SR reconstruction quality in remote sensing imagery. Furthermore, it can be combined with other edge-aware techniques to further enhance perceptual quality.

InSAR parallel processing become very attractive in recent years with the exponential growth of SAR data volume. Many InSAR parallel algorithms are deployed on cloud platforms with fixed hardware and network environments, or adopt a single granularity (e.g., scene-level or pixel-level), leading that the computing resources are not fully explored.

This research proposes a novel multi-granularity distributed parallel processing framework for time-series InSAR (TS-InSAR). The framework integrates three granularity levels (data granularity, task granularity, and algorithm granularity) and designs an adaptive scheduling strategy to dynamically adjust granularity based on task characteristics and computing resource status.

The proposed proposed multi-granularity parallel TS-InSAR processing framework has been employed to map ground deformation of the whole China territory annually since 2022, facilitating national-scale geohazard assessment.

Machine learning has become an increasingly important tool for quantitative prediction of complex mineralization patterns, offering new opportunities for improving mineral prospectivity mapping. Recent studies have shown that algorithms such as neural networks, support vector machines, and gradient boosting can capture nonlinear relationships and integrate diverse geoscientific variables with high predictive power. At the same time, traditional knowledge driven approaches such as the fuzzy weights of evidence method continue to demonstrate competitive performance, especially in geologically heterogeneous regions. This study provides a comparative evaluation of four machine learning models including logistic regression, support vector machine, backpropagation neural network, and extreme gradient boosting, together with the fuzzy weights of evidence method. The analysis is applied to a distinct environmental and geological predictor dataset from Labrador, Canada, a region characterized by complex lithological variation and limited historical exploration data. The goal of the study is to assess the robustness, stability, and generalization ability of these methods when transferred to previously unused datasets and differing geological conditions. Model evaluation is performed using cross validation, feature importance analysis, and spatially aware performance metrics. The resulting prospectivity maps highlight similarities and differences among the algorithms and identify areas with high potential for gold mineralization. The findings provide insight into the strengths and limitations of machine learning and knowledge based methods for mineral exploration and support the development of reproducible and interpretable workflows for regional scale mineral prediction.

Accurate segmentation of indoor 3D point clouds is essential for applications such as autonomous navigation, robotic interaction, and augmented reality mapping. Indoor scenes, however, remain difficult to segment due to clutter, occlusions, and repetitive structural patterns that often mislead conventional geometric or rule-based approaches. While deep learning models have improved segmentation accuracy by learning features directly from raw points, they typically require large annotated datasets and significant computational resources.

This paper presents SAMNet++, a hybrid segmentation framework that combines unsupervised segment generation with supervised refinement to achieve high accuracy while reducing annotation effort. In the first stage, a SAM-based LiDAR module—adapted from the Segment Anything Model—produces coarse, label-free segment proposals by leveraging fused LiDAR–RGB data. These proposals capture object boundaries and structural regions without manual labelling. In the second stage, a refined PointNet++ network enhances semantic precision and class consistency through targeted supervised learning.

To develop and evaluate the system, a dedicated indoor dataset was collected using a UAV equipped with a LiDAR sensor and an RGB camera, covering multiple rooms and corridor environments. Experimental results demonstrate that SAMNet++ outperforms state-of-the-art baselines in precision and F1-score, particularly when segmenting fine architectural details or navigating cluttered indoor spaces. With its balanced accuracy, efficiency, and reduced dependence on annotations, SAMNet++ offers a practical solution for real-time indoor mapping and scene understanding.

The research presented in this paper focuses on monitoring the phenomenon of urban heat islands (UHI) and provides local authorities with decision-making assistance in preventing their occurrence or mitigating the consequences of existing ones.

This paper proposes the design of a prototype design data warehouse for structured management, integration and analysis of multi-source geospatial data related to UHI detection and mitigation, focusing on two major Croatian cities: Zagreb and Split. Research in this area is the result of two started projects about UHI.

The proposed system is expected to provide a consistent and scalable framework for managing the heterogeneous geospatial datasets needed to understand urban climatic conditions. By standardising data handling and building on open data sources, the system creates the conditions for robust analysis of UHI patterns and for the development of tools that can support both research activities and the operational needs of local authorities.

Designed as a foundation for future monitoring mechanisms, planning tools and mitigation strategies, the system also aims to encourage broader use of open geospatial data in environmental and urban-climate studies. Its reproducibility and transparency should contribute to establishing a stable framework for further research and for practical applications in climate-resilient urban development.

Unauthorized land-use parcels are key targets in territorial spatial governance. Featuring diverse types, scattered distribution, strong concealment, traditional monitoring—satellite remote sensing with time lag and manual inspections with limited coverage—fails to meet the demand for rapid localization and verification.

This study proposes an automated verification framework driven by UAV hangars, integrating five links: intelligent scheduling, automatic data collection, real-time transmission, semantic interpretation, result dissemination. Adopting a "cloud-edge-terminal" architecture, it incorporates direct georeferencing, parcel segmentation, and improved A*+ algorithm-based path planning, achieving closed-loop automation of "detection-verification-evidence collection."

Field tests in Shanghai with 6 UAV hangar stations and 120 parcels showed 100% coverage, 75% less manual work, and adaptability to diverse scenarios. It addresses "slowness, omission, inaccuracy" in traditional workflows, providing a technical paradigm for data-driven territorial governance.

Accurate precipitation estimation is essential for hydrological applications and hazard monitoring in coastal regions, where complex terrain and strong land–sea interactions pose major challenges. This study investigates long-term rainfall variability and evaluates the performance of six gridded precipitation products—PERSIANN, IMERG, CHIRPS, ERA5-Land, GSMaP, and MSWEP—over the Guangdong–Hong Kong–Macao Greater Bay Area during 2001–2023. The results reveal pronounced spatial heterogeneity in precipitation trends: coastal subregions show a clear drying tendency, whereas the inland mountainous region remains comparatively stable. Despite these spatial differences, all regions exhibit synchronized interannual variability, suggesting the dominant influence of large-scale climatic drivers. All evaluated products successfully capture the unimodal seasonal cycle associated with the South China Monsoon, but notable discrepancies emerge during the peak rainy season, when intense convective rainfall leads to greater uncertainty. Among the six datasets, GSMaP and IMERG consistently outperform the others, showing higher correlation coefficients and lower RMSE across most months. In contrast, PERSIANN performs less reliably during low-intensity rainfall periods, while ERA5-Land systematically underestimates peak rainfall intensity. Overall, this study highlights the importance of region-specific evaluation of precipitation products in complex coastal environments and provides practical guidance for hydrological applications, hazard assessment, and disaster risk management.

This study presents an operational framework for the early detection of heavy rainfall based on the temporal dynamics of Cloud-Top Temperature (CTT) observed by geostationary meteorological satellites. The central hypothesis is that a characteristic “rapid rise followed by a sharp fall” in CTT serves as a precursor signature of subsequent convective intensification, as verified by radar-observed rainfall surges. The temporal pattern is analytically decomposed into the rise–peak–fall–trough phases, and the temperature drop amplitude (swing) between the peak and trough is quantified to define the WATCH (Warning and Threshold-based Convective Hotspot) window that indicates potential heavy-rain development. Two categories of lead time are formulated: the observed lead time, representing the exact temporal offset between the onset of CTT cooling and radar-detected rainfall intensification; and the estimated lead time, inferred from the gradient of the CTT decrease when radar data are unavailable or delayed. An edge-enhancement algorithm is implemented to minimize omission at the temporal boundaries, while adaptive thresholding and regional calibration enhance the algorithm’s transferability across diverse climatic and topographical environments. The proposed method is designed for real-time satellite operations and can be seamlessly integrated into existing satellite-radar hybrid nowcasting systems. By detecting convective growth phases preceding radar reflectivity increases, the method extends the effective warning lead time and improves the reliability of short-term rainfall forecasts. The findings demonstrate that CTT-based dynamic monitoring provides a physically consistent and computationally efficient tool for flash-flood preparedness, early warning, and rapid situational awareness in operational meteorological and hydrological applications.

this paper, we define a new wildfire risk prediction task from the perspective of wildfire state transition of next year, and hence, propose a novel approach named Wildfire State Transition Discrete Diffusion Model (WildfireSTDDM), that can directly capture the high-dimensional distribution of wildfire risk only through available and on hand historical wildfire events, with the following characteristics: (1) A 25-year-long-term daily wildfire historical record for British Columbia (BC) province, Canada is built deriving from the Fire Information for Resource Management System (FIRMS) with $10\text{km} \times 10\text{km}$ spatial resolution, using spatial aggregation. We define four wildfire state transition types based on the presence or absence of fire in a three-year historical period versus the fourth year: Persistent no-fire, New ignition, Fire cessation, and Persistent fire. (2) The proposed model can capture the categorical distribution of wildfire state transition type conditioning on the historical records and is trained in an end-to-end fashion, contributing to less cumulative error. (3) The proposed model can generate a high confidence map of next year's wildfire risk only through the long-term daily historical wildfire event without any other driving factors, and also correlate with the complex and stochastic wildfire pattern. (4) Since our model depicts the discrete wildfire state of each pixel forward as a discrete-time-inhomogeneous stochastic process, making it well-suited for characterizing next year's wildfire state transition uncertainty in model projections by performing multiple posterior sampling through Monte Carlo.

This study proposes a stripe removal method for remote sensing grayscale images based on ultralytics. First, we have got images from GEE, and stripes were annotated via Label Studio. Second,we have trained the ultralytics model with the annotated dataset, and adopting the best weights combined with pre-trained model for new image annotation. Finally, for stripe removal, the trained model detected stripe regions in remote sensing images and located their bounding box coordinates. Non-stripe areas were marked, with the largest normal area selected as the reference. Stripe region pixel data were segmented using detected bounding boxes, followed by histogram matching between stripe regions and the reference area to align grayscale distribution. Corrected stripe regions were replaced back to original positions to generate and save stripe-free images. This method achieves accurate stripe detection and effective grayscale correction, providing a reliable solution for remote sensing image preprocessing.

This contribution presents GEMAUT, a robust and open-source tool dedicated to the automatic generation of Digital Terrain Models (DTMs) from high-resolution satellite-based Digital Surface Models (DSMs). The paper provides a historical overview of the methods used for DTM extraction over the past twenty years, from early morphology-based filters to physically based optimization models and recent deep learning approaches. This retrospective is complemented by an analysis of the evolution of Earth-observation sensors, whose increasing spatial resolution now enables the application of LiDAR-oriented ground-filtering techniques directly to satellite DSMs.

The latest version of GEMAUT removes one of the main limitations of earlier implementations by eliminating the need for an external ground mask. Ground points are automatically extracted from the DSM using either the slope-based filter implemented in SAGA or the Cloth Simulation Filter available in PDAL. The terrain is then reconstructed through an energy-based surface optimization approach that combines robust data fidelity terms with curvature-based regularization.

A second major contribution is the introduction of a fully automatic quality assessment module. By analysing local DSM–DTM elevation differences, GEMAUT produces a spatialized precision mask that estimates the relative vertical accuracy at pixel level. This capability supports reliable quality control in operational and industrial workflows.

The tool has been fully refactored, relies exclusively on open-source libraries, and is publicly released on GitHub to encourage transparency, reproducibility, and collaboration within the ISPRS community.

The formation of canopy gaps alters forest microclimates, influencing understory regeneration, soil organic matter decomposition, and nutrient cycling, thereby playing a crucial role in forest ecology. Traditional methods for detecting canopy gaps typically rely on multispectral imagery or LiDAR data, which are accurate but costly and technically demanding. In recent years, several studies have explored the feasibility of using UAV-based RGB imagery for gap detection. This study utilized UAV RGB imagery to analyze the temporal dynamics of canopy gaps to assess the feasibility of employing RGB-based vegetation indices for canopy gap detection. The Normalized Green–Red Difference Index (NGRDI) combined with DSM differencing was used for analysis. Results show that when NGRDI < 0.03, forest areas can be effectively categorized into two classes: “canopy gaps” and “canopy cover.” The overall classification accuracy reached 93% with a Kappa coefficient of 0.68. However, the omission error was 44.44%, which suggesting that the model requires improvement in detecting small or edge gaps. It is recommended that identified threshold is used as a preliminary criterion for “canopy versus non-canopy” classification, supplemented with DSM or CHM data to improve detection accuracy.

Imagery from small satellites has been available for decades, yet automatic and accurate geometric correction remains a persistent challenge, especially when dealing with imagery which exhibit higher radiometric variability and a lower signal-to-noise ratio. This study introduces an enhanced version of the geometric processing module within the STORM processing chain, designed to perform fully automated orthorectification of images from small satellites. The module leverages publicly available ancillary data and deep learning-based road extraction techniques to eliminate the need for manual data collection and preprocessing. Ground Control Points (GCPs) are automatically generated by matching roads extracted from satellite imagery with corresponding vector roads obtained from open-access web databases. The orthorectification pipeline integrates several key components: ancillary data preparation, road extraction, GCP extraction, and final orthorectification using a digital elevation model. Experimental results on NEMO-HD small satellite imagery demonstrate that the proposed method can achieve accuracies of less than two pixel. The integration of deep learning for road detection provides a novel and effective approach for the fully automated orthorectification of satellite data of various types.

Digital Elevation Models (DEMs) are essential for terrain analysis and environmental applications, yet freely available global DEMs such as the Shuttle Radar Topography Mission (SRTM) DEM often contain noticeable elevation errors. Recent advances in space-borne LiDAR, particularly Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), provide highly accurate elevation observations for DEM correction. However, most existing studies treat DEM correction as a single regression task and pay limited attention to correction direction, although direction errors may further degrade the corrected DEM. To address this issue, this study proposes a dual-task optimization framework for DEM correction using ICESat-2 data and auxiliary topographic and environmental variables. The network includes a shared feature extraction backbone, a regression branch for estimating correction values, and a classification branch for predicting whether DEM elevation should be increased or decreased. Kent County, New Brunswick, Canada, was selected as the study area, where 35,823 ICESat-2 elevation points were used for model training and validation. Results show that the proposed method outperforms Random Forest, XGBoost, and a conventional deep neural network, achieving a root mean square error (RMSE) of 1.76 m, a mean absolute error (MAE) of 1.37 m, and a direction consistency rate (DCR) of 75.05%. Compared with the original SRTM DEM, the corrected DEM reduces RMSE and MAE by approximately 27.6% and 25.9%, respectively, and improves DCR by 1.66% over the conventional deep neural network (DNN). These results demonstrate that incorporating correction direction into the learning process can effectively improve DEM correction accuracy and directional reliability.

Recent advances in UAV-based remote sensing have made high-resolution 2D imagery widely available, however the extraction of 3D tree structure from such data remains a primary challenge. This paper presents a novel framework for deriving True Tree Crown (TTC) geometry from Pseudo Tree Crown (PTC) representations, through a graph-based learning model. The PTC is generated from single nadir RGB images by interpreting grayscale intensity as height. This serves as an intermediate 2.5D representation that bridges the gap between conventional imagery and full 3D structure. We establish a spatial correlation between PTC and LiDAR-derived TTC meshes using geometric feature extraction and correspondence analysis. Preliminary results on synthetic data demonstrate a strong correlation between PTC and TTC height distributions, confirming that PTC encodes meaningful structural information. To learn the mapping from PTC to TTC, we propose a Graph Neural Network architecture with three GraphConv layers (64 – 128 – 256 channels), residual connections, and a composite loss function combining Chamfer distance with Laplacian and edge regularization. This framework enables the estimation of complete 3D tree crowns from single RGB images, transforming vast historical 2D image archives into valuable 3D forest data for ecological monitoring, carbon accounting, and sustainable forest management.

Advancements in unmanned aerial vehicle (UAV) technology have enhanced remote sensing and photogrammetry, enabling high-resolution mapping of terrain. This study evaluated the accuracy of digital elevation models (DEMs) derived from UAV-based structure-from-motion (SfM) photogrammetry by comparing them with real-time kinematic global navigation satellite system (RTK GNSS) survey data in the Steenbras Lower Dam wetland catchment, Cape Town, South Africa. High-resolution RGB imagery was captured using a DJI Phantom 3 UAV at an altitude of 35 meters above the highest terrain point, with a ground control network shared with the GNSS survey. Pix4D software was used to reconstruct the terrain, producing digital surface models, orthophotos, and ultra-high-resolution point clouds. Accuracy was assessed using 1,502 corresponding points. Initial metrics were affected by tall vegetation in the northern and southern periphery of the wetland. After filtering out absolute differences exceeding 0.5 m, the median elevation difference decreased from 0.464 m to 0.222 m, the median difference reduced from 0.344 m to 0.217 m, and the RMSE dropped from 0.605 m to 0.260 m. These results demonstrate that UAV-derived DEMs provide reliable and precise topographic information for wetland catchment mapping.

Dholavira, India’s second-largest Harappan site after Rakhigarhi, dating from 3000–1500 BCE, is renowned for its sophisticated water management system and has attracted significant archaeological interest since its discovery in 1968. Despite decades of conventional surveys, many structures remain unidentified, constraining spatial understanding of the site. This study develops a multi-sensor, multi-platform framework using active and passive datasets (optical, microwave, and LiDAR) from satellite, UAV, and ground-based sources to support improved documentation and analysis of archaeological features. Earth Observation (EO) datasets were processed to identify surface anomalies using multi-sensor analysis, while Synthetic Aperture Radar (SAR) data were used to delineate potential subsurface zones for subsequent GPR investigations. UAV-LiDAR data were utilized to enhance high-resolution 3D surface mapping of the site. Guided by satellite-derived anomalies, Ground Penetrating Radar (GPR) surveys were conducted at selected locations to investigate subsurface features. The GPR results revealed shallow hyperbolic reflections and stratigraphic discontinuities up to ~1.5 m depth, indicative of buried structures and disturbed ground conditions, with depth estimates derived using an assumed velocity model for dry sandy soils. Terrestrial Laser Scanning (TLS) enabled high-resolution three-dimensional reconstruction of excavated structures, showing close agreement with Archaeological Survey of India (ASI) records. The results demonstrate an effective and interpretable framework for archaeological prospection and multi-scale analysis, with future potential for integrating machine learning to advance systematic site analysis and digital heritage conservation.

Runway surface deterioration poses critical challenges for aviation safety and maintenance planning. Traditional inspection techniques are often labor-intensive and localized, lacking temporal continuity for assessing long-term degradation. Previous studies have primarily focused on pavement visual distress or thermal imaging, leaving a significant gap in non-destructive, satellite-based monitoring of runway condition using multispectral data.This study addresses that gap by employing multi-year Sentinel-2 Surface Reflectance imagery (2021–2025) to evaluate surface degradation of the Deoghar Airport runway. Six spectral bands (B2, B3, B4, B8, B11, B12) were analyzed to compute four spectral indices—Aggregate Degradation Index (ADI), Composite Condition Index (CCI), Surface Reflectance Index (SRI), and Thermal Stability Index (TSI). Temporal mean composites for each January were generated and analyzed for pixel-wise trends. Results revealed from 2021 to 2025, ADI decreased from 0.0876 to 0.0789, CCI increased from -0.2069 to -0.1718, SRI rose from 1.5171 to 1.6484, and TSI improved from -0.0158 to -0.0059, indicating overall runway surface stabilization with gradual roughness increase. A mean degradation rate of 0.010 year⁻¹, with 93.5% of pixels in the moderate class, 4.3% in high, and 2.2% in critical condition. The B12 band showed the maximum mean change (289.73), while B2 exhibited the most statistically significant trends (p < 0.05 for 72.1% pixels). The findings confirm that spectral reflectance indices effectively capture physical and chemical surface transformations. This method provides a scalable, non-destructive framework for continuous monitoring of runway health and supports predictive maintenance decision-making for sustainable infrastructure management.

The ecological analysis of desertification requires knowledge of post fire regeneration in the mid-step, influenced by topographic conditions and climate parameters. The North West regions of Algeria are affected each summer by violent forest fires which last over several days and affects woodlands, natural forests and reforestation. Usually NDVI is used, other derived index from radiometric data in remote sensing are widely used to monitor vegetation dynamics. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors, and to recognise the different regeneration patterns of each burnt area. Several variables (such as climat, lithology, slope, aspect) were considered in order to analyse their possible relationship with the recovery process. Some of these variables showed a significant effect over the regeneration time, although further analyses seem still needed. Pre-fire and post-fire Landsat images and Alsat, were obtained to assess the related fire severity with using the widely-used Normalized Vegetation Index (NDVI) and modified Soil Adjusted Vegetation Index (MSAVI); Ratio vegetation index (RVI), and the index of regeneration (RI), to determine vegetation regeneration dynamics for period (2005-2007-2009 and 2015). Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. In addition, analysis of NDVI and RI stratified by pre-fire vegetation conditions and post-fire burn severity estimates could also be beneficial. And in this context post fire regeneration and topographics aspects are most important to ecological analysis of desertification in semi arids areas.

Urbanisation is accelerating globally and is a defining feature of modern cities. In 2016, 55% of the global population lived in cities, projected to reach nearly 70% by 2050. Rapid urban and population growth pose major challenges for sustainable development. By 2030, global urban land cover is expected to reach 1.2 million km²—three times that of 2000. This transformation involves significant Land Use Land Cover (LULC) changes, often converting natural vegetation into impervious surfaces like buildings and roads.

Urbanisation strongly correlates with rising Land Surface Temperature (LST) and intensified Urban Heat Island (UHI). Despite global attention to UHI, few studies have examined the spatio-temporal dynamics of LST in relation to recent urbanisation trends in Perth, Australia. As the city undergoes rapid suburban expansion and faces increasingly hotter summers, it is vital to understand how new urban development affects thermal patterns.

This study aims to address this gap by:

1. Identifying and delineating the areas of new development in Perth between 2005 and 2024,

2. Analysing and comparing LST patterns between long-established older and newly developed areas

3. Investigating the relationship between LST and its contributing factors, such as building and population density, tree canopy cover, surface moisture, albedo, and proximity to rivers

To achieve these aims, the study evaluates urban expansion between 2005 and 2024 and quantifies thermal differences using multi-temporal Landsat-derived LST.

Multimodal Remote Sensing (RS) methodologies have been increasingly studied in recent years due to their capacity to analyze multimodal RS data acquired from different sensors, thereby providing improved temporal resolution and extracting richer information than single-modal RS data. Deep Learning (DL) methodologies have accelerated the study of multimodal RS methods, thanks to their ability to learn features during training automatically. Many multimodal DL methods exploit this capability to learn a shared domain across modalities. However, most of them struggle to align heterogeneous modalities in a common representation. For this reason, we propose a supervised multimodal DL method that analyzes image time series acquired by different sensors to perform semantic segmentation. The proposed DL method is based on a Variational Autoencoder (VAE) that models the spatio-temporal information of the multimodal input image time series, with encoders and decoders composed of 3D convolutional layers, and learns the probability distributions for each modality. The probability distributions are combined to derive a joint distribution used for semantic segmentation. Learning the joint probabilistic distribution is achieved by combining the probabilistic parameters across modalities using a Product of Experts (PoE) approach. The feature maps derived from the obtained latent space are processed through three decoders. Two decoders aim to reconstruct the input multimodal image time series. The third decoder performs a semantic segmentation based on the inputs. Experiments conducted on the MultiSenGE and Austria datasets, which comprise Sentinel-1 and Sentinel-2 image time series acquired in France and Austria and representing heterogeneous classes, yielded promising results.

Rapid urbanization and climate change have the potential to negatively affect water availability in the coming decades. The Caribbean region is particularly at risk since, among other factors, large water storage facilities are not as abundant as in larger nations. It is imperative therefore, that water resources in the small island nations of this region are efficiently managed and monitored. Recent open-source, satellite earth-observation capabilities and data have presented additional tools for managers of this critical resource to better manage water and water infrastructure. In this study, we demonstrate the capacity of utilizing Synthetic Aperture Radar (SAR) data from the Sentinel-1 satellite for mapping surface area changes in three reservoirs on the island of Trinidad using a Google Earth Engine (GEE) framework. Sentinel-1 data was processed using GEE to produce average reservoir surface area calculations for each season (wet and dry) of each year for the period 2017-2023. The resultant reservoir surface area values were cross referenced against average seasonal precipitation values obtained from the CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station data) database. The approach used in this study can be integrated into existing water resource monitoring frameworks to improve efficiency at little to no additional cost.

Mikania micrantha is one of the most aggressive invasive alien plant species in low-elevation landscapes of Taiwan. This study used fixed-wing UAV imagery to monitor its flowering-season distribution in a primary monitoring area north of Liyu Lake, Hualien County, eastern Taiwan. Rather than treating the dataset as a continuous annual time series, the analysis was based on three flowering-season observation epochs acquired on 14 January 2021, 7 December 2021, and 4 January 2024. UAV imagery was collected using an eBee X platform and processed in Pix4Dmapper Pro to generate high-resolution RGB orthomosaics with an average ground sampling distance of 3.08 cm/pixel. M. micrantha patches were delineated through manual image interpretation, and kernel density estimation (KDE) was applied to evaluate changes in spatial concentration and hotspot distribution. The interpreted infestation area decreased from 2,094.74 m² in the first epoch to 1,361.94 m² in the second, then increased to 1,799.09 m² in the third. KDE results showed a similar pattern, with persistent core infestation zones and renewed expansion in surrounding areas, including a new hotspot in the southeastern part of the monitoring area. These findings demonstrate the practical value of UAV-based monitoring for adaptive invasive plant management.

This presentation examines how to improve automatic road extraction from small-satellite images, where image quality is often limited by lower SNR and higher radiometric variation. The study tests whether data augmentation with noise and blur during pretraining can make deep-learning models more robust under these challenging conditions. Using a two-stage transfer-learning setup, a U-Net with a ResNet-50 encoder was first pretrained on PlanetScope RGB imagery and then fine-tuned on data from NEMO-HD, a Slovenian microsatellite mission. Several types of synthetic noise and blur were evaluated at different intensity levels.

The availability of high-resolution LiDAR data and advances in machine learning have opened new possibilities for automating coastal infrastructure mapping. This work presents a streamlined workflow for detecting marine docks using LiDAR intensity data and Detectron2, a state-of-the-art convolutional neural network framework. The approach integrates intensity normalization, scan-angle correction, and transfer learning to improve detection accuracy across diverse environments. Applied to LiDAR tiles from British Columbia’s Sunshine Coast, the method achieved detection rates of 70–80%, significantly reducing manual digitization effort. While recall remained high, variability in precision and segmentation accuracy highlights challenges in geometric alignment. The proposed workflow offers a scalable, data-driven solution for marine infrastructure mapping, supporting applications in coastal planning, environmental monitoring, and emergency response. Future work will explore 3D kernel point convolutions to enhance spatial accuracy and leverage elevation gradients directly from point clouds.

The aerospace sector urgently requires high-fidelity, real-world simulation environments that are both current and reactive, a challenge traditional workflows fail to meet. We present a fully automated, cloud-based pipeline developed by Airbus Defence & Space to produce trustworthy, reality-based aeronautical simulation data at a global scale.

Our core innovation is the automated co-extraction of a complete object stack—including precise building footprints, vegetation, and road networks—from the same Very High Resolution (VHR) satellite imagery source. This process, leveraging a multi-model deep learning approach based on foundation model paradigms, guarantees absolute spatial and temporal coherence across all extracted features.

The extracted features are then processed to generate high-fidelity LoD 2.1 3D geometry. This is achieved using a robust geometric framework and RANSAC-based plane fitting to reconstruct complex roof structures, delivering watertight volumes and filtering out photogrammetric noise. The pipeline is fuelled by the agile Pléiades Neo constellation and will be further reinforced by the four-satellite CO3D constellation, drastically improving revisit rates and ensuring data currency.

Operational validation on a 1000 km² diverse test area confirmed the system’s scalability, achieving full Digital Twin dataset generation in under 24 hours. This workflow effectively bridges the gap between raw satellite acquisition and actionable, high-fidelity simulation environments.

Accurate terrain elevation information is fundamental for geospatial analysis and environmental monitoring. Traditional 3D survey

methods such as LiDAR and photogrammetry provide high accuracy, but are costly, time-consuming, and limited in temporal

coverage. This work introduces Finding DEM0, a zero-shot framework that converts monocular depth predictions from foundation

models into metrically calibrated Digital Elevation Models (DEMs) without requiring supervised training. The approach leverages

the geometric consistency of DepthAnything V2 and anchors it to global elevation references from the Copernicus DEM and GEDI

LiDAR data through a linear regression-based calibration. Experiments conducted on around 2,500 tiles throughout the French

territory show consistent improvements over resampled Copernicus DEM baselines (approximately 1.5 m in vegetated areas and

more than 2.0 m in urban regions). The framework thus enables frequent, low-cost DEM updates using only high-resolution optical

imagery, eliminating the need for repeated airborne LiDAR/photogrammetric acquisitions and facilitating continuous and precise

elevation monitoring.

Wildfires are among the most destructive natural hazards, posing significant threats to ecosystems, infrastructure, and human life. While satellites provide objective information for burned-area assessment, their temporal resolution is insufficient for immediate response. Conversely, social media offers rapid eyewitness reports but suffers from limited reliability, vague descriptions, and spatial inaccuracy.

To bridge this gap, this study presents a hybrid verification framework that integrates social-media-derived event information with remote-sensing imagery and deep learning. The aim is to automatically confirm fire occurrence and refine coarse social-media coordinates to pixel-level accuracy. The major innovations include:

A large-scale GEE hierarchical search to locate possible burned regions.

A dual-branch deep learning model that performs change detection with pre- and post-fire Sentinel-2 patches.

A centroid regression mechanism enabling precise geolocation correction.

With the increasing global attention to clean energy, wind turbines (WTs) play a vital role in addressing both the greenhouse gas emissions and long-term energy sustainability. Nevertheless, accurately detecting the WT installations form remote sensing images remains a challenge. Existing data sources, such as the WT points of interest (POI) from OpenStreetMap (OSM), rely primarily from volunteer contributions are often incomplete or inconsistent, limiting their reliability for scientific assessment. This study proposes a global WT detection method form high-resolution remote sensing imagery via yolov8 deep learning model. The key contribution lies in constructing a WT installation suitability map based on multi-source spatial data, which reduce the search area by 38.99%, and improve the efficiency of global WT identification. In addition, to mitigate the challenges of small-target recognition in high-resolution remote sensing images, a method incorporating projection deformation of image regions is introduced. Using this method, more than 400,000 WT targets worldwide were successfully identified. Compared with OSM records, the method achieved an accuracy of 91.67% and revealed 48,688 newly installed WTs. This work provides a valuable tool for evaluating both the current status and future potential of global wind energy development, thereby supporting sustainable energy transitions.

Reliable, comparable measures of urban green cover are essential for a sustainable urban future. We construct a global, annual 30-m fractional green vegetation cover (FGVC) dataset covering over 60,000 urban areas from 1984 onward. Using Landsat imagery in a cloud environment, the workflow adapts to each image by learning local endmember spectral signatures before applying constrained spectral mixture analysis, mitigating the influence of endmember spectral variability. Accuracy against reference maps is high (r > 0.8; MAE < 10%; RMSE < 13%), and agreement with a widely used product at 500 m is strong (r > 0.7; MAE < 12%; RMSE < 15%). We will provide pixel layers, city/regional indicators, and validity metrics to support applications including SDG monitoring, climate-adaptation planning, and equity-minded urban greening.

Cloud masking has been a critical processing step in earth observation (EO) satellite systems. Its applicability in polar regions remains difficult due to the significant challenges in the differentiation between cloud and snow areas. Despite diverse EO satellite imagery, it lacks a general approach to leverage them jointly due to the sensor dependency of most cloud masking frameworks. Vision foundation models (VFMs) offer new perspectives in realizing towards sensor-agnostic frameworks for cloud masking, however it remains under-explored and merits further investigation.

In this contribution, we propose a solution that leverages the strong feature extraction capabilities of novel foundation models for cloud masking in polar regions, building on prior works of the developed cloud masking models and the subsequent cross-sensor transferability study. The architecture mainly utilizes the pretrained self-supervised backbone from mainstream foundation models (i.e. DINOv3) and effectively adapts to downstream tasks through fine-tuning with the adaptable decoder. It also investigates text-aligned DINOv3 by incorporating pretrained text encoders to enable multimodal understanding for additional EO applications, including text-prompted identification and object query of geographic features in satellite imagery.

Compared to the prior works on the developed transformer-based cloud masking models, the VFM-based approach offers several key contributions of model capabilities, in terms of foundational backbone, sensor-agnosticity, multimodality, etc. The VFM-based multimodal approach employs advanced spectral-spatial encoding strategies compared to vision baselines for the assessment of text-alignment strategies for improved semantic tasks, establishing foundations for emerging vision-language tasks that enable trustworthy EO applications.

The rapid expansion of Earth Observation (EO) data - from multispectral/hyperspectral to SAR, LiDAR, and dense time series - offers unprecedented opportunities to understand and monitor our changing planet. Concurrently, advances in artificial intelligence (AI) are transforming how these massive, multimodal datasets can be processed, interpreted, and translated into science-based decision support. Aligned with GEO’s Earth Intelligence for All Strategy, this work presents an integrated vision for accelerating global geospatial intelligence through AI-driven EO.

The GEO AI4EO Enabler plays a central role in realizing this vision. Designed to embed AI within GEO’s broader Earth intelligence ecosystem, it brings together a global network of AI and EO experts to foster cross-disciplinary collaboration, support capacity building, and develop and disseminate reproducible, accessible AI tools. The Enabler provides a framework to standardize AI-in-EO methodologies, promote responsible and ethical AI practices, and strengthen data-driven decision-making across diverse applications. As environmental and societal pressures intensify, this coordinated approach aims to make Earth intelligence more inclusive, scalable, and impactful.

Building on this foundation, we showcase transformational AI-driven EO applications: geospatial foundation model development and benchmarking; large-scale 2D and 3D urban mapping and continuous change detection; rapid flood and wildfire monitoring using satellite time series; multi-hazard building-damage assessment; and generative AI techniques that synthesize fine-resolution observations from coarse sensors for high-frequency operational monitoring.

By coupling the GEO AI4EO Enabler’s collaborative agenda with cutting-edge AI-driven EO, this work charts a clear pathway toward democratizing Earth intelligence and enabling informed decisions for a more sustainable and resilient future.

The rocky outcrops around the Horne copper smelter in Rouyn-Noranda (Quebec, Canada) exhibit highly variable surface conditions due to a century of atmospheric emissions. These surfaces act as passive archives of heavy metal deposits, but they remain poorly mapped due to their small size, spectral heterogeneity, and frequent mixing with vegetation or anthropogenic materials. This study presents a deep learning approach for high-resolution mapping of rock outcrops and their surface condition using multisensor remote sensing data.

We combined 0.2 m orthophotos (Vexcel UltraCam Eagle), Sentinel-1 SAR, Sentinel-2 multispectral imagery, and 1 m LiDAR derivatives to classify seven surface cover types: vegetation-covered rock, degraded soil mixed with till, smooth black-coated rock, anthropogenic surfaces, smooth uncoated rock, eroded till, and rough bare rock. The training data was created from a systematic 5 × 5 m annotation grid and field observations.

A U-Net convolutional neural network was trained for semantic segmentation using RGB orthophotos and features derived from LiDAR (slope, roughness, relief shading). The model achieved an overall accuracy of 86%, with high separability between bare rock classes and moderate confusion between degraded soils and eroded moraines. Probability and uncertainty maps with a resolution of 0.2 m were created from the softmax outputs to facilitate spatial interpretation.

The resulting maps reveal distinct spatial patterns of black coatings induced by pollution and erosion processes around the smelter. This work demonstrates the potential of multisensor fusion and deep learning for detailed environmental mapping in contaminated industrial landscapes.

Intelligent optimization algorithms are powerful tools for complex geospatial computing, focusing on the exploration of key regions in the solution space. A primary application is the automated identification of optimal parameters for classifiers like SVMs, which is crucial for remote sensing. Traditional penalty methods are hindered by their empirical penalty factors: overly small values cause the search to remain trapped in infeasible regions, while excessive values divert it from the true optima, particularly under equality constraints. To address this, we reconstruct the fitness function based on the Karush–Kuhn–Tucker (KKT) optimality conditions. This formulation inherently ensures convergence to the feasible region and explicitly leverages the inverse collinearity between the objective and active constraint gradients at the boundary. Consequently, infeasible solutions are guided efficiently along a composite gradient direction toward the boundary, enabling high-precision, adaptive tracking. Our approach improves convergence efficiency and substantially reduces reliance on penalty parameters.

Polarimetric synthetic aperture radar (PolSAR) is gaining increasing attention for monitoring and analyzing urban areas and their changes, such as area extraction (Wang et al., 2024) and mapping (Wu et al., 2021). A critical foundation for the studies is the accurate characterization of urban scattering mechanisms. This task can be accomplished using polarimetric decomposition methods (Quan et al., 2023).

PolSAR systems are undergoing rapid technological developments, aiming for fine spatial resolution, wide swath, and multiple wavelengths. The development or variation of system parameters leads to changes in both the geometric and physical interaction (mechanism) of the imaging process for a radar target in urban areas in Earth observation. Then, understanding urban backscatter is challenging. In this study, we focused on the wavelength effect on the scattering mechanisms of urban targets in PolSAR data. An alteration approach has been proposed to achieve an equivalence in the decomposition results using PolSAR data across different wavelengths. After the approach, urban targets in the decomposed results exhibit consistency across the three bands , qualitatively and quantitatively. The approach is viable in reducing the impact of radar wavelength on the PolSAR decomposition result.

This study presents an integrated approach for identifying potential large-scale rock fall areas using high-resolution UAV LiDAR data collected over the Stawamus Chief, British Columbia, Canada. The methodology couples UAV-derived morphometric and structural analysis with software-based block detection and stability evaluation to delineate unstable areas in rock masses and quantify their potential failure modes. A comparison study with terrestrial laser scanner (TLS) data was also conducted to compare different remote sensing dataset resolutions and accuracy.

This study presents an integrated and user-accessible framework for SAR imagery analysis that bridges SAR data processing and AI applications. The framework focuses on three objectives: (1) establishing a standardized pre-processing pipeline for harmonized cross-sensor Level-2 products, (2) enhancing usability through a streamlined interface, and (3) demonstrating practical applications through three AI modules—oil-tank detection and geometric measurement, shoreline extraction and change analysis, and ship detection. Experimental results show that the system achieves 1.5–2× faster processing compared to manual workflows and enables consistent analysis across multi-sensor SAR data, including TerraSAR-X and ICEYE. The oil-tank module achieves 86.5% detection accuracy with sub-pixel height estimation, while the ship detection module achieves up to 100% detection accuracy under high-resolution conditions and 90% overall accuracy. Shoreline analysis demonstrates consistent detection of temporal coastal changes. These results demonstrate that the proposed framework provides a practical and scalable solution for integrating multi-sensor SAR data into AI-based operational monitoring.

The Kumtag Desert in northwestern China hosts one of the world’s most extensive fields of feathered dunes, whose continuous migration poses a direct threat to downstream oases, farmland and water resources. Yet, monitoring dune mobility in this hyper-arid environment is challenging. In this study, we develop a multi-sensor remote sensing framework that combines Sentinel-2 optical imagery and Sentinel-1 SAR data with a dense optical flow algorithm to derive high-resolution, spatially continuous displacement fields for 2017–2022. Sub-pixel displacements from COSI-Corr are used as an independent benchmark, and time series of dune migration rates are reconstructed through least-squares inversion. We further couple the remotely sensed migration rates with regional wind data to estimate sand flux and invert dune heights based on sediment mass conservation. The results reveal a persistent northeast–southwest migration of feathered dunes, with typical velocities of ~5–8 m/yr and a clear negative correlation between dune height and migration rate. The proposed framework overcomes key limitations of traditional methods and provides a transferable tool for two-dimensional kinematic analysis, aeolian hazard assessment and desertification control in complex dune systems worldwide

Earth Observing (EO) satellites are an invaluable tool in remote sensing and have various applications. Spatial resolution is often

crucial to those applications. The current work focuses on sharpening Sentinel-2 images. Moreover, a new application/program has

been developed towards this goal. The application sharpens Sentinel-2 lower resolution bands (20m, 60m) and creates a 12-band

image in 10m resolution. To run the program, one needs to load a Sentinel-2 L2A product, select one or more pansharpening methods

and click the fuse button. This process will fuse the whole scene, but it is possible to crop areas of interest and process them instead.

To validate the process, 14 pansharpening methods were employed and tested against well-known image quality metrics. On all areas

of interest, the quality indices agree with each other. However, the indices tend to penalize methods who fail spectrally, which is

correct, but they also tend to favor images with poor performance in the spatial domain. MS-SSIM seems to rank better the algorithm

images and is closer to the visual comparison assessment. HPF is one of the best performing methods for sharpening a L2A product

of Sentinel-2. ATWT, AWLP, HCS and LMM are good alternatives according to our results. The application, S-2 Sharpy (A

Sentinel-2 Image Sharpening GUI) is made available on Github. Furthermore, its generic counterpart, PanFusion (Image

pansharpening GUI for various sensors) is also made available on the mentioned platform, since it was the application that set the

foundation for the current application and study.

Thermal simulation in urban digital twins enables effective monitoring of surface urban heat islands and supports climate adaptation planning. This paper evaluates machine learning and physics based approaches for this task through a unified validation framework based on 3D point clouds applied to an urban region in Berlin. The framework enables comparison of RandLA Net for 3D point cloud processing, InfraGAN for 2D texture synthesis, and physics based simulation on triangulated mesh geometries. RandLA Net architecture is adapted for thermal prediction and tested with two feature sets: RGB only and RGB with physics derived material parameters. Deep learning methods demonstrate severe spatial overfitting: training errors are minimal (MAE less than 1 K), but test performance degrades significantly on unseen regions with MAE increasing by factors of 1.9 to 2.5. Unexpectedly, augmenting with material parameters worsens generalization, indicating inadequate feature integration. Physics based simulation maintains consistent predictions (MAE approximately 8 K) with systematic bias addressable through calibration. These results motivate hybrid approaches embedding physical constraints into neural architectures for robust urban thermal modeling.

Land surface temperature (LST) obtained from satellite observations is a key parameter for understanding Earth surface-atmosphere energy exchange and urban thermal environments. However, the use of existing satellite-derived LST datasets for urban applications is limited by the coarse spatial resolution and the mixed-pixel problem. By integrating both two-dimensional (2D) surface properties and three-dimensional (3D) urban morphological characteristics, this study proposes a machine learning-based framework for high-resolution downscaling of satellite-based urban land surface temperature (SULST). A Random Forest model was developed to generate a 1-m downscaled SULST (DSULST) map. The model demonstrates strong performance, with a Pearson correlation coefficient of 0.89, RMSE of 1.15 K, NRMSE of 0.095, MAE of 0.56 K, and an index of agreement of 0.95. The 1-m DSULST maps reveal substantial sub-pixel thermal heterogeneity that is not captured by conventional 30-m LST data. Fine-scale spatial patterns associated with vegetation, building structures, and roads are clearly resolved in the downscaled 1-m temperature maps. These results highlight a critical limitation of satellite-derived LST in representing intra-urban thermal variability. The findings demonstrate that enhancing the spatial resolution of urban LST is essential for urban applications, including modeling surface energy fluxes, pedestrian-level heat exposure, and energy consumption, all of which benefit from higher spatial resolution.

Multi-object tracking in satellite videos faces unique challenges including small object sizes, low spatial resolution, frequent cloud-induced occlusions, and dramatic scene variations across geographic regions. Existing trackers, predominantly designed for ground-based scenarios, struggle to maintain reliable identity associations when satellite imagery exhibits long temporal gaps, transient visibility losses, and shifting appearance distributions. To address these challenges, we develop AURORA-Track, an end-to-end tracking framework that builds upon the Multiple Object Tracking as ID Prediction (MOTIP) backbone tailored for satellite video analytics. AURORA-Track introduces three key innovations: (1) an uncertainty-aware ID prediction module that augments the MOTIP decoder with calibrated confidence estimation, enabling robust handling of ambiguous associations and reducing false re-identifications; (2) a cloud/shadow-aware trajectory model that explicitly detects visibility degradations and leverages historical motion context to sustain tracking under partial or prolonged occlusions; and (3) a cross-scene knowledge transfer branch that meta-learns priors across diverse urban, maritime, and rural environments and rapidly adapts to new regions with minimal supervision. Extensive experiments on public satellite video datasets, including SatSOT and SatVideoDT, demonstrate that AURORA-Track achieves state-of-the-art performance, improving HOTA and reducing ID switches compared to leading baselines. These results validate the effectiveness of combining the MOTIP backbone with uncertainty-centric, occlusion-robust, and scene-adaptive enhancements for reliable satellite video tracking.

Urban heat islands (UHIs) present a critical challenge to sustainable urban development, demanding high-resolution monitoring tools for effective climate adaptation. We address this need by implementing a machine learning framework for downscaling Land Surface Temperature (LST) data, demonstrating its ability to capture fine-scale thermal variations. The methodology leverages multi-sensor remote sensing data fusion, integrating high-resolution optical observations from Sentinel-2 with thermal imagery from Landsat 9 (daytime LST reference) and ASTER (nighttime LST reference). Random Forest (RF) regression is employed, utilizing Sentinel-2 multispectral bands, derived spectral indices (e.g., NDVI, NDBI) to characterize land cover, and a Digital Elevation Model (DEM) to account for topographic effects. The RF model was rigorously trained and its hyperparameters optimized via randomized cross-validation to predict LST at a 10-meter resolution. Results demonstrate robust performance, achieving a high R2 of 0.75 (Mean Absolute Error, MAE: 1.7°C) for daytime LST and R2 of 0.50 (MAE: 0.6°C) for nighttime LST. The resulting downscaled maps delineate pronounced heat accumulation in dense built-up areas, notably its historic center and large commercial zones, contrasting sharply with cooler vegetated areas and green urban corridors. A comparative assessment against bilinear interpolation, TsHARP thermal sharpening, and linear regression confirms that the RF framework achieves the best balance between predictive accuracy, spatial coherence with the source thermal data, and meaningful sub-pixel detail, effectively preserving the critical fine-scale thermal patterns. Ultimately, this study advances UHI monitoring by enabling the precise identification of heat-vulnerable areas, thereby supporting targeted mitigation strategies even in small and medium-sized cities.

This study aims to assess the seasonal impact of land use on daytime normalized urban heat island (DNUHI) and nighttime normalized UHI (NNUHI) in Ahvaz, one of the hottest cities in Iran. To this end, 63 corrected Landsat images acquired in 2024 were used, and daytime land surface temperature (DLST) and nighttime land surface temperature (NLST) were derived for the four seasons. Thereafter, DNUHI, NNUHI, and normalized UHI (NUHI) indices were derived by normalizing the temperature differences between urban and non-urban areas. A land use layer consisting of 14 classes was overlaid with the thermal data to investigate the role of land use type in controlling thermal patterns. The results showed that the highest DNUHI values were observed in industrial (0.14-0.20) and oil (0.12-0.19) areas, which generated the highest daytime heating. At night, the highest NNUHI values were recorded in industrial (0.12-0.24), military (0.07-0.20), and oil (0.08-0.18) land uses, indicating the strong heat storage capacity of these areas. In contrast, green spaces, orchards, and agricultural lands showed the lowest DNUHI and NNUHI values (about 0.01-0.06). These findings can inform the design of sustainable climate strategies, the development of green spaces, and land use management to reduce urban heating.

Urbanisation in Johannesburg is significantly altering local climate conditions, yet long-term, satellite-based analyses of the Urban Heat Island (UHI) effect remain limited. This study addresses this gap through a ten-year (2014–2024) spatio-temporal assessment of Land Surface Temperature (LST) patterns and their socio-economic drivers. Landsat 8 imagery processed in Google Earth Engine (GEE) provided high-resolution LST data, which were integrated with regional socio-economic indicators, including population density and poverty metrics, and analysed using Ordinary Least Squares regression to examine their statistical relationships.

Findings indicate an apparent intensification of the UHI effect, with Johannesburg’s average LST in 2024 0.79°C higher than in 2014, and a 28% increase in population. Spatial analysis identified Regions D and G as persistent heat islands. At the same time, Region B consistently remained a cool zone, reflecting the significant role of land use and land cover in shaping intra-urban temperature variations. Poverty consistently correlated with higher surface temperatures, whereas population density showed a weak or negative relationship, suggesting that factors such as vegetation cover, construction materials, and surface permeability exert a greater influence on local temperatures than population density alone. Comparative analysis with other South African cities indicates that these patterns are systemic and socio-economically driven, highlighting broader issues of environmental inequality.

The study concludes that Johannesburg’s UHI effect is intensifying and raising urgent environmental justice concerns. It recommends targeted, socially equitable interventions, including urban greening programmes, cool roofing and paving materials, and thermal resilience strategies in informal settlements, to promote climate-adaptive and inclusive urban development.

Understanding the relationship between land surface temperature (LST) and near-surface air temperature is critical for urban microclimate research, especially for fine-scale thermal assessment in heterogeneous urban environments. This study investigates the spatial and temporal coupling between satellite-derived LST and in-situ air temperature during the summer of 2024 (June–August) on a university campus characterized by mixed building forms, surface materials, vegetation, and water bodies. High-resolution LST data were derived from Landsat-8 imagery, while near-surface air temperature was measured using a dense IoT-based monitoring network consisting of 19 observation sites.

Instead of treating LST as a direct proxy for air temperature, the analysis focuses on comparing spatial rankings, diurnal variations, and surface–air temperature differences across monitoring sites to identify systematic patterns of thermal consistency and divergence. The results show that LST presents stronger spatial differentiation than near-surface air temperature, whereas air temperature exhibits smoother spatial patterns and clear nighttime convergence. Surface–air temperature differences vary systematically across environmental settings, indicating heterogeneous coupling relationships between surface and atmospheric thermal conditions.

To further examine spatial correspondences, a convolutional neural network combined with Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to evaluate whether spatially reweighted LST information better explains observed air temperature variability. The results indicate that emphasizing thermally relevant surface regions improves the consistency between satellite-derived thermal signals and in-situ air temperature observations.

Overall, this study provides an interpretable framework for analyzing surface–air temperature relationships at the micro-scale and supports more reliable urban thermal environment assessment by integrating satellite observations with ground-based measurements.

This work presents a Machine Learning approach for the automatic recognition and mapping of rare earths in Brazil. While the country holds the world's second-largest reserves, identifying these valuable elements remains a challenge. By combining reflectance spectroscopy measured in the laboratory with open-access hyperspectral satellite imagery, a specific rare earths dataset is compiled. This dataset is used to train, validate and test neural networks to correctly classifiy rare earths by their spectral signatures.This method provides a novel and efficient tool for mineral prospecting and supports the geological community in assessing the national potential of these critical resources.

Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.

This study examines land surface temperature (LST) trends and future projections in the Metropolitan Regions of Campinas and Piracicaba, São Paulo, Brazil, from 2002 to 2022. A time series of 15,091 MODIS LST images (MOD11A1 and MYD11A1 products, v6.1) was processed using Google Earth Engine to generate monthly composites, which were subsequently analyzed in ArcGIS Pro. Harmonic regression modeling identified seasonal and interannual temperature trends and simulated monthly temperatures through 2033. Eight municipalities, grouped by urban density, were selected for detailed comparison. The results indicate persistently higher LST values in highly urbanized areas, while municipalities with initially lower urbanization levels exhibited steeper warming trends over time. Projected January temperature increases between 2023 and 2033 range from 0.4°C to 1.0°C, with the most pronounced changes occurring in areas experiencing rapid land-use transformation. These findings are consistent with broader patterns of urban heat island intensification, emphasizing the combined effects of vegetation loss, impervious surface expansion, and urban densification. While the projections are statistical estimates based on historical trends, they provide valuable guidance for climate adaptation strategies and urban planning. This study demonstrates the utility of MODIS time series and multidimensional GIS analysis for monitoring and forecasting thermal dynamics in rapidly urbanizing regions.

Urban development is often accompanied by anthropogenic activities, changes in land morphology and serious damage to natural areas. Consequently, the urban climate is also affected, as temperatures are higher in urban centers and because of the presence of the urban heat island phenomenon, which poses a health threat to local citizens. The Monterrey Metropolitan Area (MMA) is the second-largest urban area in Mexico and is characterized for rapid urbanization and industrialization processes, steep climate conditions and the presence of urban heat islands. This combination makes living conditions rough for its inhabitants, especially for vulnerable groups. In order to quantify and compare heat vulnerability in urban areas, metrics such as the Heat Index measure the heat exposure and its effects on the human body. This study interpolated both relative humidity and temperature information from 15 local climate monitoring stations to determine the Heat Index for the six hottest weeks of the 2023 summer in the Monterrey Metropolitan Area. The interpolation methodologies used (IDW, Kriging and Spline) were later compared in order to cross-validate the results and define the most accurate performance base on both MAE and RMSE statistical analysis.

Operational monitoring increasingly depends on UAV imagery for safety, environmental, and infrastructure applications. Yet detecting unexpected objects remains challenging when targets blend into the background or operations extend to low-light and night conditions. Modern UAV platforms with integrated sensors now make high-resolution RGB, co-registered multispectral, and longwave infrared data more and more readily available, motivating methods that exploit complementary reflectance and thermal cues. In this paper, we address the detection of camouflaged objects by multispectral anomaly detection. We study 15 different three-channel stacks deviated from several image modalities, including real imagery and simulated longwave infrared images that encode the expected scene. This allows us to recast anomaly definition as reality–simulation discrepancy, as alternative to the conventional anomaly definition. We separately apply four detectors of differing categories to these image stacks: the classical Reed–Xiaoli Detector, a Region-of-Interest extractor, the Isolation Forest as convenctional machine learning approach, and a finetuned deep learning model. Evaluation is based on well-established metrics including precision, recall, and the F1-Score. Results reveal that combinations of near- and longwave infrared offers the best accuracy, longwave infrared alone is competitive, and simulated infrared imagery generally reduces performance, most likely due to a rather significant reality–simulation gap. We conclude that combining reflectance and thermal channels is critical for robust anomaly detection and that compact deep models currently provide the best trade-off for operational deployment.

The Siwana Ring Complex (SRC), located in western Rajasthan, India, is a distinctive geological formation characterized by its elliptical configuration. It primarily consists of rocks from the Neoproterozoic-era Malani Igneous Suite, reflecting its ancient volcanic origins. Peralkaline granitic rocks attract attention due to their potential to host valuable mineral deposits, particularly rare earth elements (REEs) and niobium (Nb). This study explores the potential of spaceborne imaging spectrometer (EMIT) and multispectral (Sentinel-2 MSI and ASTER Thermal) datasets for demarcation of REEs-bearing peralkaline granites, along with the potential sites of REEs. Silica and feldspar mapping was performed through the ASTER TIR dataset for targeting the potential sites of alteration zones within the peralkaline granites. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were applied on the EMIT and Sentinel-2 datasets for targeting the peralkaline granites of the region, which are the host rock for the REEs. The accuracy achieved through the EMIT and Sentinel-2 classified image varies. SVM and RF accuracies for EMIT are 93% and 96% respectively, while for Sentinel-2 are 95% and 99% respectively. Integrating the results from ASTER TIR with Sentinel-2 and EMIT highlighted the REEs-enriched zones within the peralkaline granites. This study demonstrated the potential of synergic use of thermal with spectral datasets for REEs delineation.

This contribution presents a novel method for infrared image stripe remover that addresses the limitations of current approaches in difficulty of extracting structural information of stripes. we design a progressive structure that sequentially aggregates contextual

information from intra-strip, inter-strip, to global levels. Specifically, a strip attention unit is proposed to harvest the contextual information for each pixel from its adjacent pixels in

the same row or column, while row attention and global attention are combined with their wide-ranging feature representation. This multi-scale attention mechanism address

local stripe artifacts and progressively incorporate broader image context in challenging conditions and provides more reliable results for practical applications. Experimental evaluations on multiple datasets demonstrate significant improvements compared to state-of-the-art methods. The method is designed to be computationally efficient and suitable for real-world deployment in fields.

The Landsat program provides over five decades of moderate-resolution satellite imagery, offering an invaluable record for monitoring land cover and land use changes. Despite its consistent calibration and open-access policy, Landsat’s low temporal resolution and frequent cloud contamination lead to sparse and irregular time series, limiting its usefulness for temporally continuous analyses. Reconstructing these missing observations is essential to improve temporal consistency and enable more accurate environmental monitoring. Previous studies, including our earlier work with the closed-form continuous-depth neural network (CFC-mmRNN), have shown promising results in modelling irregular Landsat time series. While the CFC-mmRNN achieved higher accuracy and lower computational cost than traditional methods such as continuous change detection (CCD), its performance declined under extremely sparse conditions, highlighting the need for more robust approaches. To address these limitations, this study introduces two transformer-based models for reconstructing very sparse historical Landsat time series: a one-dimensional Transformer and an enhanced three-dimensional variant that integrates a convolutional neural network (ResNet) with the Transformer architecture. The 1D Transformer processes individual sparse time series as input, whereas the 3D Transformer employs image patches (spatiotemporal cubes) to capture both spatial and temporal dependencies. Both models were applied to Landsat data (1985–2023) across the Canadian Prairies and evaluated against the CFC-mmRNN under varying spectral bands, data densities, and seasonal conditions. The results demonstrate that the Transformer-based models consistently outperform CFC-mmRNN, providing more accurate and temporally consistent reconstructions, particularly under extremely sparse observation scenarios.

Rapid Arctic warming has accelerated sea ice decline, intensifying interest in the Northern Sea Route (NSR) and the demand for reliable short-term forecasts. This study benchmarks non-recurrent deep learning models for daily sea ice concentration (SIC) forecasting over the NSR using the NSIDC-0051 SIC record (1988–2023). For each forecast, models ingest the previous 30 days of SIC on a 64 × 128 grid and predict the subsequent 10 days. Models are trained and validated with a five-fold walk-forward scheme over 1988–2020 and tested on 2021–2023. Two deployable architecture families are evaluated: CNN-based and Transformer-based backbones. To align with NSR operations, evaluation focuses on navigation-centric metrics. SIC fields are thresholded at 15% to define ice masks, and forecast skill for 1–10-day leads is assessed using Integrated Ice Edge Error, Mean Boundary Error, Intersection over Union and Anomaly Correlation Coefficient. CNN-based backbones consistently outperform Transformer-based backbones for boundary and overlap metrics across all lead times, with PoolFormer achieving the lowest errors and highest overlaps and leading short-term anomaly skill. However, the family-mean boundary error for the CNN group exceeds 30 km at a 7-day lead and 35 km at a 10-day lead, indicating that the practical utility of current models for NSR route planning is limited beyond about one week. These findings support modern CNN-based architectures for operational short-term Arctic sea ice forecasting and highlight the need for hybrid designs that preserve strong spatial feature encoding while better representing multivariate temporal dependencies.

Over the past decades, snow cover trends in North America have been analyzed, providing vital information to the Global Climate Observing System and other stakeholders about the looming signals of climate-driven snow declines. Detecting daily changes in snow parameters (e.g., snow depth, snow cover extent, and snow water equivalent) is, however, fraught with challenges, including internal variability unrelated to climate signals. We used GlobSnow's passive microwave remote sensing data and a Siamese U-Net model to compare patterns of daily changes in snow water equivalent (SWE) over the mid- and high-latitude regions of North America. The model detected changes in SWE with an F1-score of 94.8% and 100.0% in locations where it was not trained, and 99.3% at the location where it was primarily trained; this suggests the model's generalization potential to different climatologies and geographic locations. Using the model, we computed a similarity vector to compare SWE trends. We found that although lake-effect snowfall may be prevalent in the Great Lakes Basin during the winter months, the region consistently records the highest frequency of daily changes in SWE. Alaska, Yukon, and the Northwest Territories tended to have minimal daily changes in SWE, suggesting that latitudinal gradients may dominate changes in the snow regime and cryosphere's processes in the warming climate scenarios.

This contribution introduces OPTIG, an open-source Python tool for modeling glacier ice thickness and volume using Glen's Flow Law. The tool integrates geospatial inputs including DEMs, surface velocity raster, and flowline data to perform subglacial bed inversion and identify potential glacial lake outburst flood (GLOF) hazard sites. Validation against GPR measurements demonstrates ±22% uncertainty ranges. OPTIG empowers data-scarce regions with accessible, high-fidelity glaciological analysis for climate adaptation and hazard resilience.

The remote sensing of biophysical parameters in aquatic systems, such as water constituent concentrations, depends strongly on the quality of the spectral data. Sun glint, specular reflection from the water surface, is a major artifact that can substantially contaminate the remote sensing reflectance (Rrs). Accurate modeling of glint is essential, particularly in multi sensor analyses, to ensure seamless Rrs and water constituent products. We build upon the recently developed WASI AI model to mitigate sun glint effects. WASI AI is an AI assisted physical inversion framework that offers key advantages over traditional physics only approaches, including improved handling of spectral ambiguities and significantly faster inversions. We evaluate the effectiveness of WASI AI’s glint correction capability through an inter sensor consistency analysis between Landsat 9 and Sentinel 2. The analysis uses near simultaneous acquisitions over optically complex coastal waters of the Adriatic Sea. The two overpasses are only a few minutes apart, which allows to assume stable bio-optical conditions. However, sun glint can vary rapidly because it is sensitive to viewing and illumination geometry as well as wind driven surface roughness and currents. These factors may affect the data from the two sensors differently. Our results show that the WASI-AI glint correction identifies substantial differences in magnitude and spatial patterns of glint between the near simultaneous Landsat 9 and Sentinel 2 acquisitions. The Rrs consistency analysis demonstrates that, after glint correction, agreement between corresponding bands of the two sensors improves on average by 6% in R^2 and by 5% in NRMSD.

This study presents a near-real-time flood-mapping approach that integrates satellite-based Earth observation (EO) data, digital elevation models (DEMs), and machine-learning (ML) techniques. Several publicly available flood datasets were evaluated; however, none fully met the requirements for spatial coverage, data quality, and thematic diversity needed for robust model development. To address these limitations, a dedicated training dataset was constructed using Copernicus Emergency Management Service (EMS) Rapid Mapping products, comprising 38 flood events from 2022 to 2025. A modular workflow was developed to generate ML-ready datasets from satellite imagery, including data acquisition, advanced preprocessing, flood mask generation, and image tiling. Additional steps, such as co-registration, rescaling, data fusion, and masking irrelevant regions, were implemented to ensure spatial and temporal consistency across heterogeneous inputs. The developed model demonstrates reliable performance in delineating flood extents, achieving an average IoU of 0.70 on the validation dataset. Although the system remains under active development, the results indicate strong potential for operational deployment in near-real-time flood monitoring.

Rivers play a key role in the functioning of ecosystems, and their hydromorphological condition is essential for environmental assessments and water management. In France, field measurements used to evaluate channel geometry, such as bankfull width and slope, remain limited in spatial coverage due to logistical constraints. However, with the nationwide availability of high-density LiDAR data (>10 points per square metre), new opportunities have emerged for the large-scale, reproducible and automated characterisation of river morphology.

This paper introduces Bf3D, a fully automated 3D workflow designed to extract hydromorphological metrics from pre-classified LiDAR point clouds. Unlike traditional approaches based on manually placed cross-sections or 2D analyses, Bf3D relies on a continuous 3D representation of channel topography. The workflow includes automated river delineation, irregular digital terrain model (DTM) reconstruction, detrending, and a volumetric adaptation of the hydraulic-depth method to estimate bankfull stage and width.

Bf3D has been applied to over 1,400 river reaches across France. The results demonstrate accurate centreline delineation and bankfull width estimates that are close to field measurements. This approach removes user-dependent biases and enables rapid processing at a national scale. This approach introduces a new paradigm for hydromorphological monitoring by enabling the consistent, automated computation of key indicators across extensive river networks.

The present work focused on the synergistic utilization of Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (FO) derived Ensemble Terrestrial Water Storage Anomaly (TWSA), downscaled (GOU) TWSA and a gap filled JPL TWSA data and other hydrological variables to assess variability in groundwater storage (GWS) change in the last two decades (2002-2022) over Indus and Ganga river basins. The Indus basin witnessed a significant decline in groundwater with a rate of change between -2.11 to -3.0 cm/yr. Ganga basin also witnessed a significant decline in GWSA with ensemble dataset indicating a decline of -1.27 cm/yr and gap-filled JPL dataset indicating a decline of -1.88 cm/yr after removing soil moisture estimates respectively. Groundwater Storage Anomaly (GWSA) obtained from downscaled TWSA also indicated a significant negative trend. However, the magnitude of trend was considerably lower (0.9 cm/yr) than the ensemble (-1.37 cm/yr) and gap filled (-1.88 cm/yr) datasets. Ground observations also indicated a decline in GWSA in Ganga basin with a rate of change -0.26 cm/yr. GWSA computed from downscaled TWSA and satellite derived soil moisture showed highest positive corelation (R = 0.78) and least RMSE (17.5 cm) with in-situ GWSA in the Indus basin during 2002-2022. Similar results were observed for Ganga basin where downscaled TWSA (R=0.87) showed satisfactory corelation and low RMSE (8.11 cm) indicating that GOU TWSA and European Space Agency (ESA) soil moisture derived GWSA was able to capture the localized groundwater storage change effectively.

The Indus River Basin, a major Himalayan river system, has complex topography limiting availability of in situ measurements, which obstruct reliable characterization of precipitation patterns, thereby negatively affecting climate impact assessments and water resource management strategies. Understanding hydrological processes and efficiently managing water resources and dangers in Himalayan river basins depends on accurate high-altitude precipitation estimation. In this study, we have used the satellite-based precipitation reanalysis dataset (ERA5) and gauge-based data of IMD to overcome this issue. To conduct the analysis, we have used statistical methodologies, which include correlation analysis, root mean square error, probability of detection, and critical success ratio. We assessed the performance and detection of precipitation from ERA5 in comparison to IMD for the high altitudes. The performance evaluation of ERA5 precipitation against IMD bservations indicates a reasonably good agreement between IMD and ERA5 datasets in representing precipitation patterns over the study region, with R² = 0.793 and RMSE = 47.831 mm. The POD = 0.9686 and CSI = 0.7507. These results suggest that ERA5 provides a reliable representation of rainfall variability over the study area and can be effectively used for regional climate and hydrological applications. Further, we evaluated the performance of a gauge-merged precipitation dataset (GSMaP_ISRO) to highlight the significance of gauge merging over the study area. It was observed that the dataset outperformed in all the statistical indices. This study affirms the reliability of satellite-based precipitation datasets in high-altitude Himalayan regions and provides critical insights for sustainable water resource management in the face of evolving climatic conditions.

This study focuses on assessing the physical and chemical parameters of Indus River water

collected from a single sampling location, with special emphasis on seasonal variations and

sample preparation for ICP–MS analysis. The objective is to evaluate how water quality and

sediment inflow vary across different seasons and to determine the concentration of dissolved

and particulate matter in the river system. Water samples were collected regularly from the same

site of the Indus River during the summer, monsoon, and winter seasons. The analyzed physical

parameters include temperature, pH, oxidation-reduction potential (ORP), dissolved oxygen (DO

in mg/L and % saturation), electrical conductivity (EC), total dissolved solids (TDS), and

salinity. These parameters help in understanding the physicochemical condition of the river and

its environmental status. Temperature and DO show seasonal dependency due to changing flow

and temperature conditions, while EC, TDS, and salinity indicate variations in ionic

concentration and evaporation rate.

Spectrometry) analysis to estimate trace and heavy metal concentrations. In addition to field

observations, Remote Sensing and GIS techniques were used to analyze spatial variations in land

use, vegetation cover, and watershed characteristics influencing the Indus River. Satellite data

(Landsat and Sentinel) were processed in QGIS and Google Earth Engine to detect seasonal

changes in turbidity, surface temperature and land cover. The study concludes that the Indus

River water exhibits clear seasonal variations in its physical parameters and sediment load.

The cryosphere is essential for maintaining the balance of Earth's climate; however, it faces growing threats from increasing anthropogenic activities, including industrial emissions, biomass burning, and vehicular pollution, which have led to significant deposition of pollutants like ash on snow surfaces. These pollutants, originating from local industries, forest fires, and traditional wood-burning practices in the region, are altering the natural snow properties and accelerating disasters, snowmelt processes, potentially affecting climate, water resources, and local ecosystems. This research examines the effects of ash contamination on snow reflectance in the Himachal mountainous region of India, utilizing hyperspectral data collected through an XHR 1024i spectro-radiometer. The analysis involved a detailed examination of prominent absorption features, first derivative assessments, calculations of relative absorption strength, albedo evaluations, and the application of Principal Component Analysis (PCA) to thoroughly investigate the spectral alterations resulting from ash deposition. The need for this study arises from the growing concern over the accelerated melting of snow and glaciers due to reduced albedo caused by impurities like ash. The analysis indicates that the absorption feature at 1025 nm exhibits a pronounced sensitivity to ash contamination, demonstrating a reliable decline in relative absorption strength as ash concentration increases. The first derivative analysis highlighted rapid changes in reflectance, aiding in the identification of absorption features, while principal component analysis indicated that more than 99% of the spectral variance can be attributed to ash concentration. Albedo analysis supported the observed spectral alterations by confirming a notable decrease in snow reflectance.

The Chad Basin is a major water source for more than 30million people across four countries in the arid Sahel. Understanding long-term groundwater changes in the Chad Basin is necessary for water security, abstraction management and transboundary cooperation. In this study, we employed GRACE satellite and GRACE-FO satellite data (Total Water Storage Anomaly, TWSA) along with GLDAS land surface modeling to determine Groundwater Storage Anomaly (GWSA) trend between year period 2002 and 2024. The findings reveal water hydrological paradox as the basin shows a significant TWSA increasing trend of +5.91 mm/year (R² = 0.70). But, the gain is decoupled from replenishable reserves which are declining for the Surface Water/Soil Moisture (-1.04 mm/year) and near GWSA stagnation (+0.24mm/year, R² = 0.02). The rainfall shows a weak association (+1.65 mm per year trend) with GWSA (r = -0176). From this, it appears increasing rainfall is ineffective for recharging the deep aquifer. The excessive use of humans contributes to the localized depletion of the severe GWSA in the western margins, primarily in northeastern Nigeria. The present findings indicate that rather than climate variability, it is the failure of governance. That water scarcity is due to our unsustainable human activities and the inefficient water recharge pathways. In order to implement spatially-explicit abstraction quotas and prioritise effective high efficiency Managed aquifer recharge schemes, the data is essential for LCBC.

Monitoring dynamic alluvial rivers is essential for safe inland navigation, yet traditional bathymetric surveys are often costly and infrequent. This study presents an automated, cost-effective methodology for detecting and monitoring migrating sandbars by integrating Sentinel-2 satellite imagery with daily water gauge data.

Implemented within Google Earth Engine (GEE), the algorithm matches specific river water levels with cloud-optimized satellite scenes. It utilizes the Sentinel Water Mask (SWM) index to separate water from sediments, applying a 30-meter internal channel buffer to mitigate mixed-pixel errors along the shorelines.

The automated extraction was validated against high-resolution (3-meter) PlanetScope imagery. The results demonstrated high geometric agreement (mean Intersection over Union = 0.71) and a strong area correlation (R² = 0.97). While the 10-meter spatial resolution of Sentinel-2 introduces a systematic 26% overestimation of the sandbar areas , this over-segmentation serves as a beneficial safety margin in a navigational context, preventing the underestimation of submerged obstacles.

By correlating specific gauge levels with the emergence of sandbars, this method provides a vital 2D spatial baseline that enables the estimation of available water columns over specific bottlenecks. Ultimately, this approach supports the continuous generation of spatial databases, offering a practical foundation for dynamic relative depth mapping within River Information Services (RIS).

Snow cover plays a critical role in the hydrology and climate of the Himalayas, serving as a vital water reservoir for millions of people. Most previous studies often placed limited emphasis on recent country-scale assessments along with detailed snow variability. This study assessed the spatio-temporal dynamics of snow cover in Nepal during 2024 using 8-day MODIS snow cover products at 500 m resolution. Monthly maximum snow composites were generated to quantify snow cover fraction, seasonal trends, persistence, and variability. Results show distinct seasonal variation, with mean snow extent highest in winter (42.97%) and lowest in autumn (26.55%). Monthly snow cover peaked in April (50.01%) and reached a minimum in November (22.31%), reflecting strong intra-annual variability. Snow persistence mapping revealed that 32.32% of Nepal experienced no snow throughout the year, whereas 6.91% remained snow-covered year-round, corresponding to high-altitude permanent snow regions. The snow status change analysis highlighted dynamic snow behavior, with over 60% of pixels experiencing one or more transitions, underscoring the sensitivity of transitional snow zones. These findings improve understanding of snow variability in complex terrain and provide a scientific basis for hydrological modeling, water resource planning, and climate change adaptation in Nepal, where snowmelt-driven runoff is a key contributor to river discharge.

Due to global warming, Himalayan glaciers are retreating rapidly by several metres annually leading to the expansion of glacial lakes and increased risk of glacial lake outburst floods (GLOFs). These changes pose serious threats to downstream communities, highlighting the urgent need for climate adaptation and disaster preparedness. Gya Glacier, in particular, forms a moraine-dammed lake that experienced a significant outburst in 2014. The lake’s area expanded 1.25% in between 2018 to 2024, indicating a gradual increase and sustained hazard potential. To assess this risk, an integrated approach was employed using remote sensing, geographic information systems (GIS), and two-dimensional dam-break modelling with HEC-RAS. Multi-temporal satellite data from Sentinel-2 and High-resolution images were used to monitor changes in lake area, volume, and surrounding land use/land cover. High-resolution topographic data supported hydrodynamic modelling, allowing simulation of flood propagation and identification of vulnerable zones. The simulation revealed that a sudden lake breach could inundate approximately 1.71 ha of agricultural land, 1.28 ha of built-up area, 1.04 ha of fallow land, and 0.06 ha of a national highway. The greatest flood depths and velocities were recorded in the upper reaches due to steep gradients, with major damage concentrated downstream. To mitigate such risks, establishing an early warning system is crucial. This can include installation of Wireless Remote Terminal Units (WRTUs), Automatic Weather Stations (AWS), and GLOF detection systems at the lake site. Key sensors may include radar level sensors for monitoring water levels, and meteorological sensors to track climatic and hydrological changes in real time.

Rapid and accurate acquisition of the bathymetry of large-scale nearshore shallow sea is of great significance for coastal economic development, safe navigation of ships and coastal ecological protection. Beihai and Fangchenggang of the Beibu Gulf of Guangxi as the research area. Three inverse algorithms are firstly using for the bathymetric inversion experiments, which are one-band model, two-band-ratio model and multi-band-combination model, based on Landsat-9 images and electronic chart data. After that these three inverse algorithms of water depth are compared and then analyse the accuracy of the bathymetric inversion between the unzoned and zoned ones. The results of the experimental results that the multi-band-combination model exhibit the highest inversion accuracies in both experimental areas among the MAE and RMSE are 1.3843 m and 1.7611 m in Beihai and that of Fangchenggang is 1.8609 m and 2.4599m; following the bathymetric stratification, the average weighted errors of water depths are reduced, which mean MAE and RMSE reduced in the Beihai region by 0.6414 m and 0.8031 m and the mean MAE and the RMSE decreased by 1.6788 m and 1.9163 m The multiband combined regression model had a superior effect after the bathymetric layered inversion.

Monitoring global water storage dynamics is essential for understanding the impacts of climate change on hydrological systems. The Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) missions have provided a unique opportunity to quantify Terrestrial Water Storage (TWS) variations at large spatial and temporal scales. However, differences among GRACE solutions from various processing centers, such as CSR, JPL, and GFZ, can lead to uncertainties that must be carefully assessed for reliable interpretations (Wang and Li, 2016).

This study aims to provide a comprehensive analysis of global TWS changes from 2002 to 2025 by integrating multiple GRACE-derived TWS products. Spatial trends of TWS were calculated to identify regions and countries experiencing significant water gain or depletion. Furthermore, monthly TWS variations were extracted to construct time series for individual countries, enabling the detection of long-term hydrological patterns and seasonal fluctuations. An uncertainty assessment was also performed to evaluate the robustness of the estimated trends and temporal variations.

Mount Kilimanjaro, an East African water tower, is undergoing hydro-climatic and land use changes with uncertain impacts on water availability along its elevation gradient. This ongoing study integrates satellite remote sensing, physically based hydrological modelling, and community knowledge to characterise spatial patterns of water availability and compare them with local experiences. Land use and land cover (LULC) are mapped using the European Space Agency (ESA) WorldCover 10-m product; vegetation dynamics are analysed with leaf area index (LAI); and climate forcings are derived from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and ECMWF Reanalysis v5 (ERA5) temperature. We implement the Soil and Water Assessment Tool (SWAT) to simulate water yield by elevation band, in the absence of streamflow, model evaluation uses independent remotely sensed constraints from the Global Land Evaporation Amsterdam Model (GLEAM) evapotranspiration (ET) and ESA Climate Change Initiative (CCI) soil moisture. Semi-structured interviews and surveys across three elevation zones capture perceived change and adaptation strategies. Preliminary analyses indicate heterogeneous trends, with the largest declines in lowland catchments and more variable responses at mid- and high elevations. Ongoing work will quantify uncertainties (forcings/LULC/parameters) and translate findings into elevation-specific measures for climate-resilient water planning.

This research focuses on exploring the capabilities of the SWOT satellite mission’s Ka-band Interferometric Radar (KaRIn) sensor for retrieving lake ice and overlying snow properties. SWOT KaRIn Version D Pixel Cloud Data Products are compared to in-situ snow and ice measurements on Łù'àn Män (Kluane Lake) during the Calibration and Validation phase that took place over a three month period in 2023. The Snow Microwave Radiative Transfer (SMRT) model is used to simulate backscatter for varying snow and ice scenarios to better understand variances in observed backscatter across the lake. Optical satellite acquisitions are also utilized to extract and compare backscatter to surface reflectance to analyze seasonal lake ice phenology trends. Preliminary results indicate that KaRIn-retrieved heights are inconsistent during the winter season. Additionally, the contrast in backscatter for ice and open water allow for effective ice cover mapping. During the winter season, backscatter values exhibit a general negative pattern, with SMRT simulations indicating a correlation to snow cover variability.

This study investigates the use of Landsat data for monitoring water clarity, expressed as Secchi Disk Depth (SDD), across the Finger Lakes region in New York. SDD, a long-established indicator of water clarity, is measured using a Secchi disk and widely applied in limnological research. Recent advances have enabled remote sensing-based estimation of SDD, with Landsat imagery frequently used alongside band ratios to mitigate atmospheric effects. Cloud-computing platforms such as Google Earth Engine (GEE) further support large-scale water clarity assessments by providing accessible Top-of-Atmosphere (TOA) and Surface Reflectance (SR) products.

The study uses citizen-science SDD measurements from the NY-DEC CSLAP program (2017–2023) across all 11 Finger Lakes. Corresponding Landsat 8 TOA and SR reflectance values are extracted from GEE using a 3×3 mean around sampling points and filtered for clouds and shadows. A Random Forest model is trained using both original bands and band ratios to estimate SDD under multiple evaluation schemes, including 80:20 train–test splits and 5-fold cross-validation with both random and stratified sampling.

Results show that stratified sampling yields more reliable predictions due to variability among lakes, and TOA performs slightly better than SR in this case. Feature-importance analysis indicates consistent influential band ratios across products. The study provides the first Landsat-based assessment of water clarity for all Finger Lakes and supports improved understanding of water quality trends in these socioeconomically important freshwater systems.

This work presented a data-driven and scalable approach for performing inland water bathymetry by integrating SAR-derived shoreline dynamics with water-level observations. The method leverages the high temporal resolution of Sentinel-1 imagery and diverse water-level data sources to infer relative elevation and uncertainty estimates.

By exploiting non-uniform sampling theory and regression-based interpolation, the method establishes a foundation for automated, reproducible bathymetry using globally accessible data. Future work will address error modeling and validation against high-resolution reference datasets.

Based on the integration of multisource satellite observations, including GRACE/GRACE-FO gravimetry, altimetry, steric, and sediment datasets, this study provides a comprehensive analysis of sea level changes and their driving mechanisms in the East China Sea (ECS) over the periods 1993–2022 and 2002–2022. The findings reveal that the regional mean sea level rise is predominantly driven by manometric changes (mass addition), contributing approximately 87% (3.06 mm/yr during 2002–2022), while steric effects account for only about 12.6%. A pivotal discovery is the critical role of substantial sediment deposition from major rivers like the Yangtze. This deposition introduces a net bias of –0.35 mm/year in GRACE-derived mass trends, and correcting for this "sediment effect" is proven essential for accurately closing the regional sea level budget. Decadal analysis further reveals significant variability: the ECS sea level rise rate was notably high at 6.51 mm/year (1993–2002), sharply decreased to 2.45 mm/year (2003–2012) primarily due to a strong negative thermosteric contribution (–1.53 mm/year), and subsequently recovered to 4.19 mm/year (2013–2022). At the seasonal scale, annual variations are dominated by steric effects, whereas semiannual signals are primarily controlled by manometric changes. This study successfully demonstrates that the ECS sea level budget can be closed within uncertainty when sediment corrections are applied, providing a robust methodological framework that is highly applicable to other sediment-rich coastal regions globally for improved sea level budget assessment.

Accurate, timely, and explainable Pan-Arctic sea ice concentration (SIC) maps are essential for climate change studies, Arctic sea route navigation, and climate adaptation of Northern communities. Every day, a large amount of active and passive microwave satellite imagery are collected by remote sensing systems over the Pan-Arctic region, including Synthetic Aperture Radar (SAR) from the RADARSAT Constellation Mission (RCM) and Sentinel-1, and Passive Microwave (PM) radiometry from the Advanced Microwave Scanning Radiometer 2 (AMSR2). While advanced DL-based data fusion models leverage extensive SAR and PM imagery to produce high-resolution SIC estimates, their decision making process is opaque and difficult to interpret. This study provides the first feature importance evaluation of SAR and PM inputs to improve the efficiency and transparency of using an advanced Transformer architecture for Pan-Arctic SIC mapping during the melting season.

This study evaluates the performance of five deep learning (DL) segmentation algorithms for retrieving lake ice cover from dual-polarization Sentinel-1 SAR imagery. Lake Hazen, located in the Canadian High Arctic, was selected as a representative site due to its strong climate sensitivity and variable ice conditions. A six-year dataset (2015–2021) comprising over 1,100 dual-polarization EW-mode SAR images was used to train and validate U-Net, U-Net++, SegFormer, DeepLab v3+, and PSPNet models. Binary ice–water labels were manually annotated to support model development. Temporal cross-validation using independent test years (2015, 2018, and 2021) was conducted to assess model generalization across different ice phenology periods, including ice-on, break-up, ice-free, and freeze-up phases. Results show that all models achieved high accuracy (>98% overall accuracy) during stable ice and open-water periods, while segmentation performance decreased during freeze-up due to mixed ice-water backscatter signatures. Visual analysis confirmed that each architecture successfully captured the spatial distribution of lake ice, though some misclassifications were observed in noisy or low-backscatter regions. The findings demonstrate the potential of segmentation-based DL models for automated lake ice monitoring and highlight the need for further model refinement to improve performance during transitional periods. Future work will extend the framework to additional lakes and multi-year datasets to enhance operational monitoring of lake ice.

The Surface Water and Ocean Topography (SWOT) mission

represents a major advance in Earth observation by providing the

first global two-dimensional measurements of surface water

extent and elevation. Its potential for hydrology, climate

monitoring, and water resource management is widely

recognized; however, recent studies indicate that its performance

varies across hydrological contexts.

This study presents a review of SWOT’s capabilities for inland

water monitoring based on a synthesis of published validation

studies, simulation experiments, and case applications. To

support a structured interpretation of these findings, a Strategic

Assessment Framework (SAF) is applied. The SAF is an

analytical framework that organizes the evaluation across four

components: strengths, limitations, opportunities, and risks,

enabling a systematic comparison of SWOT performance under

different environmental and observational conditions (Figure 1).

For large rivers and lakes (≥1 km²), SWOT meets its design

accuracy targets (Bazzi et al., 2025). However, in fragmented

wetlands and narrow channels, retrieval errors increase

significantly, with reported RMSE values of 30–70 cm in

simulation studies (Bergeron et al., 2020). Environmental

heterogeneity, including shoreline complexity and wind-induced

surface roughness, further increases uncertainty in elevation

retrieval (Bergeron et al., 2020), while vegetation and turbidity

reduce water–land separability and limit effective pixel

availability (Frasson et al., 2021).

The SAF highlights performance variability and identifies the

role of multi-sensor integration (Sentinel-1/2, Landsat, Planet

Scope) in improving the reliability of SWOT-based inland

water monitoring

Accurate monitoring of the Arctic Marginal Ice Zone (MIZ) is critical due to rapid Arctic Amplification. This study evaluates discrepancies between two widely used Level-4 reanalysis datasets—NOAA OISST and CMEMS L4 Arctic Ocean—over the Arctic (>58°N) from 1988 to 2022, specifically focusing on the MIZ (SIC 0–50%). After spatial reprojection to a common 0.25° grid, the comparison revealed significant discrepancies, particularly in the transition zone (SIC 15–50%). While both datasets exhibit long-term warming, CMEMS-L4 shows a much stronger warming trend (+1.173°C/decade) compared to OISST (+0.215°C/decade). This divergence is primarily attributed to algorithmic disparities: CMEMS-L4 incorporates Ice Surface Temperature (IST), resulting in higher variability, whereas OISST relies on proxy SST estimates. Crucially, a distinct temporal discontinuity was identified in OISST around 2005, coinciding with a change in its sea ice input source from NASA to NCEP. This structural break caused abrupt shifts in SIC values and even resulted in contradictory cooling trends in parts of the Greenland Sea, whereas CMEMS-L4 indicated widespread warming. These findings highlight that data processing methodologies induce non-negligible uncertainties. We recommend caution when utilizing OISST for long-term analysis in the MIZ due to its 2005 discontinuity.

Decades of fire suppression and exclusion in Jasper National Park (JNP), Alberta, Canada have altered forest conditions. Previous plot-level fire history analyses indicate a mixed-severity fire regime was disrupted after 1915 (Chavardès et al., 2018). Biomass (fuel) has accumulated, and stand connectivity and homogeneity have increased (Chavardès & Daniels, 2016). Furthermore, a mountain pine beetle epidemic has killed a significant portion of lodgepole pine within the park, shifting biomass distribution from the canopy as needles and branches drop (Talucci & Krawchuk, 2019) Under these conditions, fires can burn more intensely, with more high severity impacts, including substantial biomass loss (Hagmann et al., 2021; Harris & Taylor, 2015; Kreider et al., 2024). Understanding how altered fuel structures correspond to biomass loss is important for predicting future fire impacts, and informing forest management decisions (Schoennagel et al., 2004).

The 2022 Chetamon Fire in JNP provides an opportunity to study biomass loss using available pre- and post-fire airborne laser scanning (ALS) data. Fuel structures are determined following LidarForFuel protocol (Martin-Ducup et al., 2024). Pre- and post-fire outputs are differenced to determine spatial variability of biomass loss. Pre-fire ALS is further used to map pre-fire environmental conditions that influence fire intensity, and thus, biomass loss. This includes topography characteristics, and forest metrics such as density (Kane et al., 2007; Parks et al., 2012). These factors are analyzed as predictor variables of biomass loss in Random Forest analyses.

Warming air temperatures and prolonged periods of drought have increased fuel availability and fire activity across northern boreal forest regions. Modelling fuel structures, such as canopy fuel load, vertical distribution and spatial connectivity, is important for providing inputs in fire behavior models, as well as furthering our understanding of the environment. The overall aim of the project was to determine the efficacy and accuracy of three standard fuel modelling methodologies at high- (>30 pt/m2) and low- (<10 pt/m2) point densities and resolutions (5m, 10m, 20m, and 30m) in a dense forested environment near Fort Simpson, Northwest Territories. All metrics are compared to fuels measured in situ. This study highlights both the potential and limitations of scalable lidar-based fuel mapping and can help inform management practices, fire behavior applications, and future operational fuel hazard-mapping and risk-mitigation strategies.

A precise evaluation of positional uncertainty is crucial to maintaining the reliability of geospatial data, as well as supporting high-quality outcomes in professional surveying and mapping projects. This paper thoroughly examines the origins of error and the statistical and geodetic principles underlying accuracy assessment for technologies such as photogrammetry, airborne LiDAR, and mobile mapping systems. Building on these foundations, the study outlines a robust, methodical framework that enables practitioners to rigorously quantify the positional accuracy of their geospatial products. The approach is aligned with the most recent edition of the ASPRS Positional Accuracy Standards for Digital Geospatial Data, ensuring compliance with current industry benchmarks.

Urban green spaces including green roofs, parks, urban forests, community gardens and private green spaces are integral to city landscapes, offering ecosystem services and enhancing urban aesthetics. By leveraging data captured from satellite or aerial imagery, spectral analysis using indices such as Normalized Difference Vegetation Index (NDVI) enables effective mapping of vegetated surfaces in such urban green spaces. However, topographic views alone present certain limitations in this context, particularly for applications requiring the differentiation of vegetation based on vertical stratification. This study presents a novel approach that enables two-dimensional (2D) and three-dimensional (3D) visualization of rooftop vegetation using a combination of multispectral and digital elevation data.

Canada's boreal forest accounts for 28% of the world's boreal forest ecosystem and is a large carbon sink. Under climate change, the severity and frequency of wildland fires in this area is increasing. This is resulting in large amounts of carbon being released in to the atmosphere, affecting the rate at which climate change occurs. LiDAR is being used more frequently for studying wildland fires and has shown some success in measuring fuel consumption, providing insight into the amount of carbon emitted. This research aims to refine the methods used to process LiDAR data collected before and after a fire in the boreal forest. Different ground point filtering algorithms, methods of spatial alignment, downsampling values and DTM resolutions are explored. Findings demonstrate how the choice in data processing can influence how well LiDAR-based DoB estimates agree with field-based observations and highlight considerations to be accounted for in similar future work.

In this study, we compared existing national wetland maps with ground-truth polygons in four areas of interest located in Eastern Canada. By comparing the methods used for each map, we identified important elements to consider when producing a wetland map using remotely sensed data: 1) the five Canadian Wetland Classification System (CWCS) classes (bog, fen, swamp, marsh, shallow water) are broad and can create spectral confusion. It is preferable to use wetland subclasses and then merge them into the broad classes. 2) It is important to add SAR imagery to the classification, given that this imagery can detect many wetland characteristics related to the site's wetness and vegetation structure. 3) Ancillary data such as DEM, topographic metrics, and canopy height model are a valuable addition to the classification. 4) It is recommended to use multi-seasonal images to consider the seasonal and temporal variation in the vegetation phenology and in both surface and groundwater levels. 5) Images used should have a spatial resolution small enough to have a minimum mapping unit to be able to detect small landscape features; and 6) it is recommended to have a dense network of ground-truth sites representative of the AOI. Our study showed that mapping wetlands at the scale of Canada is very challenging, due in part to the diversity of wetland types, which complicates the definition of standardised wetland classes, as well as to the logistical challenges related to obtaining data at the Canadian scale.

This presentation will review past and recent progress in iceberg detection from space and motivate the development of a large iceberg database for future testing and comparison of the new detection techniques. The presentation also review work done to leverage ESAs Sentinel missions to build such a database.

test

test

This contribution presents a practical workflow for accurate and cost-effective forest terrain mapping in Japanese forests using a UAV equipped with low-cost LiDAR and GNSS. Instead of relying on a local reference station, we exploit the Centimeter-Level Augmentation Service (CLAS) of the Quasi-Zenith Satellite System "Michibiki" and integrate it with LiDAR-based SLAM to obtain dense terrain information with absolute coordinates.

In the proposed pipeline, LiDAR odometry estimated by FAST-LIO is aligned with the CLAS-based GNSS trajectory and fused in a pose graph on SE(3). The resulting optimization problem is solved in GTSAM using prior, odometry, and GNSS position constraints to compensate for the drift that accumulates when SLAM is used alone during large-scale flights.

Field experiments were conducted in real forest environments on multiple days and flight routes using a UAV-LiDAR system. Ground control points measured by post-processed kinematic GNSS were used as references to evaluate mapping accuracy. The results show that the integrated optimization reduces horizontal drift and improves terrain reconstruction to sub-metre accuracy, while keeping the system setup simple and low cost. The proposed approach is a promising option for operational forest surveys and other environmental applications that require frequent, wide-area terrain monitoring.

This abstract, authored by researchers from the University of Study of Pavia, DICAr, Laboratory of Geomatics, presents a comparative analysis of the geometric quality and cloud noise of four SLAM scanners. The study compares systems from different price points

Current deep learning models for UAV object detection often suffer from "context-blindness," leading to high false positives (logical fallacies, like misidentifying building features as vehicles) and low-confidence false negatives for occluded objects. To address this, this paper proposes the innovative Geo-Visual Fusion (GVF) enhancement strategy, which leverages the rich, deterministic geo-spatial prior knowledge embedded within High-Definition (HD) city maps.

The GVF approach is implemented as a lightweight, plug-and-play framework featuring a Geo-spatial Contextual Reasoning (GCR) Module. First, a Real-time Geo-spatial Registration module accurately projects initial 2D detections onto the city's unified geographic coordinate system using UAV GPS/IMU data and camera parameters. The GCR Module then performs two key functions: Logical Error Elimination, which uses a Semantic Compatibility Matrix to suppress detections that violate real-world spatial constraints (e.g., vehicles detected on building facades); and Low-Confidence Boosting, which employs a Bayesian approach to significantly raise the confidence scores of reasonable detections located in compatible geo-spatial contexts (e.g., partially occluded vehicles on a road).

Validated on a high-resolution urban dataset, the proposed framework (Baseline + GCR) consistently demonstrates improved mean Average Precision (mAP), successfully eliminating geographically implausible false positives and enhancing the True Positive Rate for low-confidence targets. This method offers a practical solution to transition from purely data-driven feature matching to context-aware semantic understanding in urban aerial perception.

Localization within a global reference frame is critical for the safe operation of UAVs. It is typically realized through GNSS measurements, however when signals are jammed, spoofed, occluded or reflected, this approach can lead to errors or fail. As most UAVs are equipped with cameras, absolute visual localization using georeferenced map representations offers a promising alternative. The recent invention of Gaussian Splatting introduces new opportunities for this task, leveraging real-time rendering from novel views to establish 2D-3D correspondences for pose estimation. In this work, we investigate the use of Gaussian Splatting maps for absolute visual localization of UAVs with a particular focus on geometric accuracy and its impact on the accuracy of position estimation. Through experiments with real-world data, we show that an initialization with dense Structure from Motion point clouds does not improve geometric accuracy compared to sparse initialization under the current training scheme. Additionally, constraining the position optimization of Gaussian Splats shows potential for improved pose estimation but introduces challenges during training. Despite these limitations, our results demonstrate the feasibility of Gaussian Splatting-based absolute visual localization for UAVs.

Uncrewed Aerial Systems (UAS, drones) are rapidly evolving technologies, with growing expectations for fully autonomous operations, enabling flights without onsite human control and Beyond Visual Line of Sight (BVLOS). A recent innovation is technology of ‘Drone-in-a-Box’ (DiaB) a.k.a. drone docks. DiaB systems provide an automated solution that integrates robust drones hosted in weather-resistant docks with typically also with cloud integration to data processing. Such connectivity enables utilization of real-time data products using both onboard and cloud processing workflows. This combination of robotics, AI, and data management holds the potential to deliver significant breakthroughs across diverse application scenarios. Objective of this study is to calibrate and assess the geometric performance of a novel multispectral (MS) DiaB system for environmental monitoring applications. The results indicated that the MS DiaB system delivers reliable performance without ground control points. For applications requiring cm-level accuracy, the post-processed georeferencing workflow was essential, whereas the direct georeferencing approach provided adequate accuracy for many operational scenarios. Our future work will extend this methodology to environmental applications.

This study investigates cross-domain generalization, adaptation behavior, robustness under visual degradation, and adaptive model selection for image-based object detection in autonomous driving scenarios. Two state-of-the-art detectors, YOLO11 and RT-DETR, are analyzed due to their complementary architectural paradigms, representing convolutional and transformer-based approaches, respectively.

The proposed framework consists of four stages: (1) zero-shot evaluation of COCO-pretrained models on the KITTI dataset to assess domain shift, (2) fine-tuning under short and extended training regimes to analyze adaptation dynamics, (3) robustness evaluation using synthetically degraded images simulating real-world perception challenges, and (4) the development of an image-based selection model for adaptive detector arbitration.

Experimental results show that YOLO11 demonstrates stronger zero-shot generalization and faster early adaptation, while RT-DETR achieves higher performance after extended fine-tuning, indicating superior long-term representation capacity. Under visual degradations, model performance varies depending on distortion type and training regime, confirming that no single detector consistently outperforms the other.

To address this, a lightweight selection model based on image quality features (brightness, blur, entropy, and edge density) is proposed to select the most suitable detector per image. The results demonstrate consistent performance improvements over individual models, achieving higher mAP without increasing computational cost.

This work highlights the effectiveness of adaptive, context-aware perception pipelines and demonstrates that exploiting model complementarity is a practical strategy for improving robustness in real-world autonomous driving systems.

This paper presents a novel methodological framework for georeferenced traffic monitoring that bridges the gap between image-based vehicle detection and geospatial analysis. Traditional video-based traffic monitoring systems operate exclusively in image space, limiting their utility for applications requiring physical measurements and integration with geospatial datasets. We address this limitation by developing a comprehensive camera calibration approach that leverages readily available geospatial data including smartphone video, drone-derived orthophotos, and 3D point cloud data.

The methodology establishes precise mathematical relationships between image coordinates and real-world geographic coordinates through a hierarchical calibration algorithm for camera parameter estimation. Ground control points are strategically selected from orthophoto and point cloud data, emphasizing features that are precisely identifiable and geometrically advantageous for calibration. The framework enables transformation of image-space vehicle detections to geographic coordinates, facilitating physical measurements, spatial analysis, and direct comparison with simulated traffic data.

Experimental results demonstrate the effectiveness of our approach, achieving a mean reprojection error of 2.94 pixels across calibration points. A case study of multi-lane traffic monitoring showcases the practical utility, where vehicle detections are successfully transformed from image to geographic coordinates, enabling lane-specific traffic analysis and potential integration with traffic simulation models. The proposed methodology offers a robust workflow for urban planning by connecting conventional video surveillance with geographic information systems, using only commonly available data sources and equipment, making it accessible for widespread implementation in intelligent transportation systems.

Reliable geometric verification is essential in the construction industry, particularly for large-scale maritime infrastructures where deviations can critically affect functionality and safety. The emerging Scan-vs-BIM approach enables automated quality assessment by comparing as-built point clouds with as-designed BIM models. It allows evaluation of the entire structure, rather than just specific points, but relies heavily on accurate spatial registration. This paper presents an evaluation of several Iterative Closest Point (ICP) variants for fine registration within a Scan-vs-BIM framework dedicated to construction tolerance verification. Three ICP variants are compared in terms of convergence behavior, robustness to noise, and stability using synthetic point clouds derived from maritime structures. The methods are then tested on real datasets, each acquired under different conditions, leading to varying data quality. Based on the results, a hybrid method is proposed to improve registration reliability. The results show that the proposed approach improves the inlier rate by 8–9% while reducing the mean deviation by approximately 1 cm on the noisiest datasets, compared to the classical point-to-plane ICP.

The automatic generation of detailed Level of Detail 3 (LOD3) building models (Gröger et al., 2012), featuring wall surface features such as windows, doors, and balconies, remains a significant challenge within urban 3D modeling. This challenge is particularly pronounced in high-density urban environments like Hong Kong, where complex building geometries, severe data occlusion from dense high-rise structures, and diverse architectural styles collectively create exceptionally difficult conditions for automated processes. In response to these challenges, this study proposes and develops a novel, adaptive workflow designed to efficiently generate semantically rich and geometrically accurate LOD3 models. Our methodology leverages multi-source data, including a large-scale repository of existing LOD2 models, Airborne Laser Scanning (ALS) data, and Mobile Laser Scanning (MLS) data, to overcome the limitations of any single data source.

This paper presents a methodology for automated semantic segmentation and 3D reconstruction of industrial building elements from unstructured point clouds. It addresses components such as roof panels, floors, rafters, purlins, and columns by combining orientation-based filtering, projection onto characteristic planes, morphological analysis, and optimization-based I-profile fitting. The workflow includes preprocessing with axis alignment and outlier removal, surface-orientation-based subdivision, contour extraction from binary projections, and automatic estimation of roof slopes and panel inclinations to guide structural reconstruction. The approach provides a systematic framework for precise digital modeling of industrial buildings, enabling efficient structural analysis, documentation, and planning.

Accurate damage localization is essential for infrastructure inspection, but conventional segmentation methods rely on dense pixel-level annotations that are costly to obtain and difficult to scale. This paper presents a foundation model-based pipeline for data-efficient damage localization in built infrastructure. The proposed workflow combines DINOv3 features for image-level classification, Grad-CAM for weak localization, and the Segment Anything Model (SAM) for prompt-guided pixel-level segmentation. The resulting masks are further transferred into 3D space for spatially contextualized visualization.

The pipeline is evaluated on two case studies. On a subset of Sewer-ML, three representative sewer defect classes are used to compare pretrained backbones and to qualitatively assess downstream localization. The DINOv3-based classifier achieves a higher average F2-score than a Google ViT baseline, reaching about 0.72 versus 0.64. On a custom historic masonry dataset, the method is quantitatively evaluated for material-loss segmentation using manually annotated test masks. The proposed heatmap-guided prompting strategy achieves a mean Dice score of 0.69 and a mean IoU of 0.53, while the classification stage reaches an F2-score of 0.99. A proof-of-concept experiment further demonstrates that segmented damage regions can be visualized within a larger local 3D scene. Overall, the results show that the proposed foundation-model based pipeline can support data-efficient and spatially meaningful damage localization across different infrastructure domains.

3D point clouds have been widely used in civil engineering, providing comprehensive geometric data for structural health monitoring, scene understanding, surface defect assessment, and more. However, the mainstream point cloud data acquisition sources, i.e., TLS and MLS, are superior for large-scale scene understanding and analysis but challenging for fine-scale analysis, particularly in laboratory testing, due to their low resolution.

This study proposes a close-range photogrammetry-based workflow for the 3D reconstruction and visual inspection of rubberised concrete (RuC) beams in an indoor-lab environment. High-resolution image sets were captured with both a Canon 5D Mark IV DSLR camera and an iPhone 14 Pro Max, and 3D models were generated in Agisoft Metashape. The comparison between reconstructed models revealed that the DSLR-based reconstruction achieved sub-millimetre resolution and texture, demonstrating satisfactory performance for fine-scale surface monitoring. An RGB-guided crack extraction method was developed to enhance the identification of surface defects to isolate the potential crack area from the background. The extracted crack regions were visually distinguishable and provided a well-structured geometrical representation of defect morphology. Furthermore, a before-and-after deformation analysis was conducted, which provides a sub-millimetre level comparison in different stages. The results confirm that the proposed workflow based on close-range photogrammetry is a flexible, intuitive, and high-resolution alternative to LiDAR-based methods for surface inspection and deformation monitoring in laboratory environmental concrete specimens. This workflow provides another aspect of structural assessment and establishes a foundation for future high-accuracy 3D feature characterisation, which can be integrated with material design and mechanical performance evaluation.

This contribution presents a Scan vs BIM–based framework for geometric quality monitoring that integrates large-scale site-acquired point clouds with design BIM models in a distributed processing environment. The approach targets both vertical structural components and complex mechanical, electrical, and plumbing (MEP) systems on active building sites. Large point clouds from terrestrial laser scanners are indexed using an octree structure, while structural columns and MEP objects are extracted from IFC-based BIM and converted into mesh representations that serve as analysis units. For each component, nearby scan points are clipped, filtered, and locally registered to the corresponding BIM mesh to compute horizontal deviations, verticality, and installation discrepancies without assuming specific cross-sectional shapes or component types. The workflow is parallelized across multiple nodes and threads so that the same procedure can be consistently applied to thousands of objects in project-scale datasets. By automating component extraction, point-cloud preprocessing, and deviation calculation, the framework enables quantitative tolerance checks and systematic identification of elements requiring inspection or rework during construction.

Floods are natural hazards triggered by intense rainfall and are particularly destructive in low-lying areas such as floodplains. In flood-prone regions, effective disaster management relies on prevention, monitoring, and emergency response strategies. In this context, remote sensing, especially Synthetic Aperture Radar (SAR), has become indispensable for flood mapping and monitoring due to its ability to acquire data under adverse weather conditions and persistent cloud cover. Multi-temporal SAR imagery processed into RGB composites allows rapid visualization of inundation patterns, while the Geographic Object-Based Image Analysis (GEOBIA) approach improves the classification of flooded areas through the integration of backscatter thresholds and terrain elevation data. This study investigates the spatial and temporal dynamics of flooding in the Lower Tubarão River Sub-basin (LTRSb), southern Brazil, following an extreme precipitation event that produced 260 mm of accumulated rainfall between May 24 and 25, 2019. The Sentinel-1B SAR images, acquired pre- and post-event, were used to map flooded areas with an overall classification accuracy of 88%. The results indicate that three days after the event, flooding covered 140 km² (29%) of the LTRSb, predominantly affecting agricultural (86.3 km²) and pasture areas (47.6 km²). The flooded extent decreased to 62 km² after 15 days and to 15 km² after two months, with agricultural land consistently accounting for 97% of the flooded area. Urbanized areas (≈1 km²) were also impacted, indicating significant risks to infrastructure and public health. These findings highlight the importance of SAR-based flood monitoring for risk assessment and disaster management in hydrographic basins.

Post-seismic deformation reflects the crustal adjustment to stress perturbations induced by earthquakes and may lead to modifications of ongoing deformation patterns. While such effects are well documented for moderate-to-large events, their detectability and significance after low-magnitude earthquakes remain poorly understood. Here, possible deformation pattern modifications associated with the February 2022 ML 3.5 Brentonico earthquake in Northearn Italy are investigated using ground-deformation time series derived from the European Ground Motion Service (EGMS) Ortho products. The earthquake occurrence is treated as a temporal discontinuity, enabling the estimation of pre- and post-event vertical/horizontal differential velocities during 2019-2023. A dual-weighted interpolation scheme, accounting for both spatial proximity and time-series reliability, is applied to derive spatially coherent maps of kinematic modifications. The results reveal measurable and spatially organized changes in deformation patterns, including localized accelerations/decelerations and direction reversals. A clear spatial correspondence between differential velocity anomalies and mapped fault systems suggests that the earthquake acted as a localized stress perturbation, modulating pre-existing tectonic structures. This study demonstrates the capability of EGMS datasets to capture post-seismic deformation signals and highlights the importance of considering low-magnitude events in long-term deformation analyses.

Underground transportation networks are essential for mobility in densely populated cities, addressing urbanization challenges such as traffic congestion, noise, and air pollution. Ensuring the safety and reliability of these infrastructures requires structural health monitoring (SHM) systems, which detect faults, deterioration, and damage. While traditional in-situ monitoring techniques provide real-time data, they are often economically restrictive. Synthetic Aperture Radar Interferometry (InSAR) offers advantages for large-area, long-term monitoring and has been successfully applied to various infrastructures, including dams, bridges, highways, and subways.

This study investigates surface displacement along a 15.4 km metro line with 11 stations in the Gebze district of Kocaeli, Türkiye, using multi-temporal InSAR. Sentinel-1 SLC IW data acquired between January 2019 and October 2025 were processed using MiaplPy, generating 504 interferograms with a 6-day temporal baseline. The phase-linking workflow utilized Persistent Scatterers (PS), Distributed Scatterers (DS), and Statistically Homogeneous Pixels (SHP), combined with Combined Phase Linking (CPL) algorithm and SNAPHU for phase unwrapping, to obtain reliable displacement time series and mean deformation velocities.

Results indicate line-of-sight displacements ranging from –10 to 10 mm/year, with the highest movements near the first station. Time series analysis shows stability from 2019 to 2021, a sudden displacement from 2021 to mid-2022, and stabilization until 2025. Monitoring these deformations provides insights into construction-induced dynamics and enables early detection of potential risks. Incorporating additional data, such as lithological, soil, and geotechnical information, can enhance data-driven monitoring. Long-term deformation monitoring ultimately supports the development of sustainable urbanization strategies and contributes to safe, resilient underground infrastructure management.

While polarimetric interferometric SAR techniques provide a strong theoretical framework for forest height retrieval, their application using C-band Sentinel-1 data is challenging due to repeat-pass acquisition geometry and strong temporal decorrelation. In this study, we assemble a globally distributed dataset combining Sentinel-1 interferometric observations with spaceborne LiDAR forest height measurements from the GEDI and ICESat-2 missions. More than 1800 Sentinel-1 interferometric image pairs were processed and spatially matched with LiDAR observations across tropical, temperate, and boreal forest regions. Sentinel-1 Single Look Complex data were used to derive interferometric coherence and polarimetric–interferometric observables, enabling statistical analysis of their relationship with forest structural properties. The results reveal physical relationships between Sentinel-1 coherence and canopy height across multiple forest biomes, indicating that Sentinel-1 interferometric measurements, under near-zero spatial baseline conditions, retain measurable sensitivity to vegetation structure despite temporal decorrelation effects. These findings provide a conceptual basis for exploiting similar repeat-pass interferometric observations from new low-frequency SAR missions such as NISAR and and upcoming ROSE-L for forest height mapping. In addition, the assembled dataset provides a global benchmark for developing and evaluating data-driven approaches for forest height estimation using Sentinel-1 observation.

This contribution presents an integrated monitoring framework that combines Sentinel-1 multi-temporal InSAR (MTInSAR) with geological, hydrological, and operational datasets to analyse long-term ground deformation in the Epe cavern storage field, Germany. Using the SBAS approach, vertical and horizontal deformation components are derived for the 30 km² storage area, revealing a bowl-shaped subsidence feature and distinct east–west deformation patterns associated with cyclic cavern operation. The analysis incorporates geoscientific information from GeoBasis NRW, AGSI+, ELWAS, and BÜK/BK50 into a harmonized GIS environment. This enables correlation of SAR-derived deformation fields with cavern-pressure cycles, soil characteristics, and groundwater variations, providing process-oriented interpretation of subsurface–surface interactions. Deformation results will be updated to include data through October 2025. The study demonstrates how MTInSAR, combined with geoscientific knowledge, supports transparent deformation assessment and contributes to the development of geospatial digital twins for subsurface infrastructure, improving operational resilience and environmental risk management.

Accurate land-cover mapping in heterogeneous metropolitan regions requires robust methods capable of overcoming limitations of optical imagery, particularly under persistent cloud cover. This study investigates the potential of L-band ALOS-2 PALSAR-2 SAR data for operational land-cover classification over Istanbul, Türkiye, by integrating advanced machine-learning algorithms, feature-selection strategies, and hyperparameter optimization. Four classifiers Random Forest (RF), XGBoost, LightGBM, and a shallow Artificial Neural Network (ANN) were evaluated using full-polarization SAR observations and a reference dataset derived from Sentinel-2 composites, orthophotos, and LPIS parcel boundaries.

Pre-processing included radiometric calibration, Lee filtering, terrain correction, and extraction of GLCM texture metrics from HH and HV channels, yielding an initial 20-feature set reduced to 18 through correlation and variance filtering. A LightGBM-driven Recursive Feature Elimination (RFE) procedure identified an optimal subset of ten features. Model optimization employed Bayesian hyperparameter tuning (TPE) under stratified 5-fold cross-validation to ensure reproducibility and generalization.

Results show that LightGBM achieved the highest accuracy (OA = 85.1%, κ = 0.81), followed by XGBoost (83.6%), RF (81.4%), and ANN (78.9%). Water surfaces were consistently the most accurately classified class, while confusion primarily occurred between urban and bare surfaces. Hyperparameter tuning improved F1-scores across all models, and reducing the feature stack to ten variables enhanced performance without loss of class separability. HV-derived texture features, particularly Entropy and Contrast, provided the highest discriminative power.

The study demonstrates that optimized feature selection and systematic hyperparameter tuning significantly enhance SAR-based land-cover classification, offering a transferable workflow for large-scale metropolitan mapping.

Mining induced deformation in underground metal mines poses a threat to surface infrastructure, underground access and environmental safety, and needs reliable spatially continuous monitoring. In this contribution, a long-term interferometric SAR analysis over an underground hard rock metal mines (Mine-B) in Khetri Copper Belt, India using TSX/TDX high resolution SAR data is carried out. Coherent small baseline DInSAR time series (CSB-DTS), stacking DInSAR, and single reference PSI chain are implemented. Stacking DInSAR derived average LOS deformation velocity and single-reference PSI derived velocity are obtained for mine-B for dataset of January 2023- December 2024. The obtained results verify that Mine-B is substantially stable while having a persistent fast and slow deformation concentrated inside and around the trough in SoZ-2 in Mine-B. The workflow shows how the combination of CSB-DTS, stacked DInSAR and PSI can facilitate the operational subsidence monitoring and long-term stability evaluation in complex mining environments and gives and gives an indication for the future integration with in-situ measurements and numerical models.

Spatially accurate fire danger information is critical for reliably predicting fire ignition probability, spread potential, and behaviour. However, Canadian fire management agencies mainly predict fire danger using weather stations, which only collect observations at explicit spatial points and cannot accurately model the fine-scale spatial variability of moisture across large and remote areas. This study predicts and maps the drought code, a variable representative of the moisture of deep, slow drying, compact organic matter across the landscape of British Columbia using ALOS-2 polarimetric SAR. A random forest model predicted the drought code of target areas with high accuracy to values derived from weather stations. The model was applied to forested areas across a time-series of ALOS-2 images on a grid-by-grid basis at a one square kilometer resolution and predicted the occurrence of fine-scale differences in drought code associated with differences in topography and elevation. The development of this drought code prediction model will allow fire management agencies to predict spatially accurate, fine-scale differences in drought code across the densely forested and highly mountainous landscape of British Columbia, improving fire behaviour and fire prediction systems.

Monitoring water levels in small and remote reservoirs is critical due to the climate crisis and rising water demands. Traditional in-situ gauge networks often provide sparse or inconsistent coverage, especially in remote regions. Satellite altimetry provides a global alternative, but it is frequently limited by long revisit times or coarse footprints unsuitable for smaller water bodies. Existing SAR-based methods face inherent limitations: amplitude-based approaches rely on accurate external Digital Elevation Models, whereas interferometric techniques are affected by coherence loss and phase-unwrapping ambiguities. To address these limitations, this research introduces a novel approach for estimating water level changes using SAR amplitude data without relying on prior morphological information. By modeling the coastal zone as a set of distinct planar slopes, the method relates the vertical water level change to the horizontal shoreline shift specific to each slope, observed as changes in the satellite range direction. The stack's standard deviation image is used to identify low-slope areas, where the horizontal response to water level variations is most pronounced. In these regions, area-based image matching is applied to quantify displacements within the coregistered stack. Finally, a least-squares estimation is used to determine temporal water level changes and local coastal slopes. The method was validated on Lake Trasimeno, Italy, using a stack of 30 Sentinel-1 images acquired in 2022. Comparisons with in-situ gauge data demonstrated high reliability, achieving an accuracy of 4 cm and a Normalised Median Absolute Deviation of 9 cm. The preliminary results are promising, while further experiments are currently underway.

This paper investigates wavelength-adaptive baseline design for spaceborne repeat-pass SAR tomography (TomoSAR) through a controlled simulation framework comparing representative X-band and C-band configurations. The study focuses on how radar wavelength influences the trade-off among vertical resolution, temporal decorrelation sensitivity, sidelobe behaviour, and baseline sampling efficiency. Using a discrete TomoSAR forward model, several experiments are conducted to analyse reconstruction performance under identical aperture, varying coherence conditions, different baseline sampling strategies, joint aperture-spacing design scans, and noise perturbations. Quantitative results show that X-band provides a clear intrinsic resolution advantage under coherent conditions, particularly for closely spaced scatterers, but this advantage degrades more rapidly under temporal decorrelation. C-band, while offering lower nominal resolution, exhibits more stable performance across coherence loss, wider design-space tolerance, and stronger robustness in noisy conditions. The comparison of uniform, minimum-redundancy, and irregular baseline patterns further indicates that baseline optimization is more critical for X-band than for C-band. The study moves beyond the general statement that “X-band is higher resolution whereas C-band is more robust” by providing experiment-based and frequency-dependent baseline design guidance. The findings support practical acquisition planning for future repeat-pass TomoSAR missions and contribute to a more quantitative understanding of wavelength-dependent sampling design.

In order to preserve their quality, SAR data are usually used in their native plane, the slant range. However, it is sometimes necessary to link ground data and radar data. Today, ground range or terrain-corrected data are frequently used for this purpose. An alternative to this approach is to project the data into slant geometry, which allows both the superimposition and co-registration of data from different sensors and the preservation of the resolution and phase of the SAR data.

An Observational Definition of the Absolute Phase in Radar Interferometry

The absolute phase in radar interferometry is required for topography and displacement estimation but lacks a general definition. The conventional definition states that absolute phase is proportional to the range difference between primary and secondary acquisitions. This definition is appropriate for simple targets such as point targets, but it cannot be directly applied to general targets.

Here, a universal observational definition of the absolute phase is proposed. It applies to any mode and does not require any assumptions about scattering mechanisms. For differential interferometry, the absolute phase is obtained by temporally unwrapping the phase as the intermediate secondary acquisition time progresses from the primary to the secondary acquisition time. This definition requires a continuous series of interferometric phase measurements along a pre-specified absolute path. The absolute phase matches the wrapped phase modulo 2π and agrees with the conventional definition for point targets.

This contribution discusses several implications of this general definition. The absolute phase of a complex target need not, and sometimes cannot, be proportional to the range difference. An example involving a permafrost landform demonstrates that the absolute phase following a cyclic change is nonzero and cannot be interpreted as a range difference. Another consequence is that the absolute phase in an interferogram can show 2π discontinuities, even when the interferogram itself is continuous and the coherence high.

This general definition enables thorough evaluation of InSAR processing chains and supports interpretation of observations.

Because of its high reproductivity, PolSAR is a suitable sensor for change detection in urban areas. Although many methods of change detection have been proposed, methods focused on polarimetric states transformation are rarely adapted. Through eigenvalue calculations, polarimetric basis which maximizes the polarimetric sensitivity can be calculated, and if this basis is fixed, quantitative change detection is available. The result from this method shows the obvious change in the target area, which is ‘Umekita project 2nd’ in Osaka city. However, changes outside the target area were also larger so that the change detection was not very effective from a relative viewpoint. To solve this problem, algorithms which surpass unnecessary changes in urban areas should be developed, and deeper understanding of scattering mechanisms in urban areas is needed.

The Amazon region has experienced increasing pressure due to the expansion of mining, especially illegal alluvial mining, driven by rising gold prices and a lack of economic opportunities. In Ecuador, this activity has contributed significantly to deforestation and the alteration of water systems, affecting river stability and water quality. The increase in suspended sediments and the modification of river channels generate ecological, economic and social impacts, including production losses and increased vulnerability of riverside communities. In this context, monitoring through remote sensing and Geographic Information Systems has become an essential tool for assessing river dynamics and the effects of illegal mining in the Amazon biome. This study analyses changes in land use and land cover in the Nangaritza River, considering an intense rainfall event that occurred in 2025. Cloud-free mosaics were generated using Sentinel-2 images, spectral indices were calculated, and supervised classification using Random Forest was applied to establish seven coverage categories. The results show a notable expansion of mining areas and sand deposits, accompanied by a reduction in forest cover. The transition matrix revealed significant losses of forest transformed into mining soil and turbid water, as well as an increase in sedimented areas downstream. The analysis of river dynamics identified five critical areas of mining expansion associated with increased sedimentation, turbidity, and morphological alterations to the riverbed. These changes reflect the growing anthropogenic pressure on the river and the need to strengthen monitoring systems to mitigate environmental impacts.

This study investigates the rapid transformations in land use and land cover (LULC) within Lahore District, a phenomenon with profound implications for ecological sustainability and land-use governance. Analysing these dynamics is crucial for minimizing adverse environmental impacts and promoting sustainable urban development. The primary objective is to assess historical LULC patterns over 30 years (1994–2024) and to simulate potential changes for the years 2034 and 2044 using an integrated CA-Markov modeling approach supported by GIS techniques. Landsat imagery from multiple sensors (TM and OLI) was processed through supervised classification methods, achieving classification accuracies exceeding 90%. The temporal analysis revealed marked changes, notably a substantial increase in built-up areas by 359.8 km², alongside reductions in vegetation cover (198.7 km²) and barren land (158.5 km²). Water bodies exhibited minimal variation throughout the study period. Future LULC scenarios generated via the CA-Markov hybrid model demonstrated strong predictive performance, as evidenced by a kappa coefficient of 0.92. The projections indicate continued urban Expansion primarily at the expense of green and undeveloped areas. These findings emphasize the pressing need for sustainable land management practices and provide a robust decision-support framework for urban planners. By integrating predictive modeling into planning policies, this research helps align developmental objectives with environmental conservation in rapidly urbanizing regions like Lahore.

This study evaluates the residential expansion of Guayaquil between 2016–2020 and 2020–2024 using Sentinel-2 imagery and supervised classification with the Support Vector Machine (SVM) algorithm. Given the rapid land transformation in tropical coastal cities, the research applied the Built-up Area Extraction Index (BAEI) to enhance the detection of built-up surfaces and distinguish them from vegetation and bare soil. The integration of BAEI and SVM allowed the development of an accurate, replicable, and low-cost approach to monitor the city’s urban growth.

Three periods of Sentinel-2 images with low cloud coverage were processed, re-projected, classified, and validated. Classification included four thematic classes (residential, vegetation, bare soil, and water bodies) using 1,000 training samples distributed across the city. All classifications achieved over 85% overall accuracy and a Kappa index of 1.0, confirming the model’s robustness in heterogeneous urban environments.

Spatial analysis of land-use transitions revealed that residential growth is concentrated in peripheral sectors such as Ciudad Santiago, Mucho Lote 2, Mi Lote, Trinipuerto, and areas near Narcisa de Jesús Avenue. Results indicate a strong tendency toward contiguous expansion, forming residential corridors along major road networks and the Guayas River. However, dispersed peripheral nuclei highlight challenges for service provision and environmental sustainability.

Overall, the combination of Sentinel-2 imagery, BAEI, and SVM proved highly effective for detecting built-up areas in tropical contexts, offering a scalable methodology for monitoring urban expansion in Latin American cities.

Hedgerows, a traditional agroforestry practice, are declining in Europe, threatening biodiversity and climate control. To support high-quality agricultural carbon credit certification, a method for automatic hedge detection using Pleiades Neo 30cm satellite imagery was developed.

Two methodological approaches were tested in three French study areas with varied landscapes: (i) a classic image segmentation using NDVI, Green Cover Fraction, and LiDAR-derived Digital Height Model, and (ii) a foundation model retrained on 150 annotated tiles with diverse landscape and satellite acquisition configurations. The methods were compared using quantitative (Intersection Over Union, Omission & Commission errors) and qualitative indicators.

The foundation model demonstrated superior hedge detection and robustness across different landscapes. A ground truth dataset based on stratified random sampling and equal allocation was created to allow the quantification of its accuracy using standard accuracy metrics. It achieved a precision of 0.89 and a recall of 0.83 for the hedge class. It effectively adapted to the morphological and ecological diversity of hedges, with few commission errors primarily due to confusion with isolated trees or linear vegetation, and omissions mainly in discontinuous or degraded hedges.

The study confirms the relevance of Pleiades Neo for detecting thin-scale elements like hedges, the effectiveness of foundation models with limited reference data, and their potential for large-scale hedge mapping. Future work aims to incorporate more spectral bands and expand the model's training to detect hedgerows across the European Union under various satellite acquisition contexts, paving the way for operational tools in agricultural carbon credit valuation.

This study focuses on county-level areas with high-density wind farm distribution in the Xing'an League of Inner Mongolia, China. Using high-resolution satellite imagery from 2016 to 2024, land cover information within and around wind farms was extracted through visual interpretation, and the spatiotemporal dynamics of land cover in these areas were analyzed. The results indicate that: (1) From 2016 to 2024, land cover change in the study area was primarily driven by wind farm expansion, which increased cumulatively by 130.87 km² (+260.35%) and exhibited the highest dynamic degree among all land cover categories (LK = +32.54%/yr). (2) Grassland was the most severely impacted land cover type, with 78.74 km² converted to wind farm land, accounting for 59.66% of the total newly established wind farm area, while cultivated land and forest land contributed 20.06% and 18.33%, respectively. (3) As wind power expanded, the land cover composition within wind farms shifted from a cultivated land–grassland balance toward grassland dominance. (4) Areas subjected to temporary disturbance from wind farm construction activities tended to recover progressively, with cultivated land exhibiting a faster recovery rate than grassland.

Addressing the United Nations Sustainable Development Goals, particularly sustainable cities and communities (SDG 11), and

the protection of terrestrial ecosystems (SDG 15), is closely linked to understanding patterns of urbanization. Rapid urban growth

significantly influences ecosystem functions, including transportation, housing, and economic development. Monitoring this growth

and analyzing performance patterns are essential for supporting decision-making and guiding urban planning and management. This

study presents an automatic approach for monitoring urban expansion by applying the Random Forest machine learning classifier

from 2015 to 2025 using Google Earth Engine. The method exploits spectral indices not only for unsupervised classification but

also for training the Random Forest classifier, thereby ensuring a fully automated workflow. The proposed approach is applied

to Gharbia Governorate, a region which lacks surrounding desert margins and is instead entirely composed of fertile agricultural

land, to monitor urban expansion in three-year intervals. The proposed study, which achieved a kappa coefficient exceeding 0.96

across all study periods, revealed a gradual decline in agricultural land from 75.5% in 2015 to 72.7% in 2025. These outcomes offer

valuable insights to support evidence-based planning and promote sustainable land use management.

Cropland abandonment (CA) is a major land-use change with important environmental and socio-economic implications. This study evaluates cropland abandonment detection using multi-temporal Landsat features and a spatially independent validation framework, comparing the performance and spatial behaviour of Random Forest and XGBoost classifiers. A set of temporally aggregated spectral indices (NDVI, BSI, NDBI, and MNDWI), including multi-year trends and variability measures, was integrated into a 56-band composite dataset. Training and validation samples were generated using 100 × 100-pixel windows centred on land-use parcels, with overlapping areas between different reference classes explicitly excluded to avoid label ambiguity. To reduce spatial autocorrelation, the data were split into separated training (1,582.6 km²) and testing (719.2 km²) areas within the Savinjska statistical region in Slovenia. Random Forest (RF) and XGBoost (XGB) classifiers were trained and evaluated using spatially separated validation data. Classification performance was assessed using overall accuracy, user’s and producer’s accuracy, and F1-score. Results indicate that XGB achieved a higher overall accuracy (0.705) compared to RF (0.670) and exhibited strong sensitivity in detecting cropland abandonment, while RF produced more conservative and spatially stable estimates of abandoned cropland area. Spatial error maps and area-based comparisons reveal systematic differences between the two classifiers, particularly in their tendency to overestimate abandonment extent. The findings highlight the importance of spatially explicit validation strategies, careful reference data preparation, and multi-temporal feature design for robust cropland abandonment mapping. The main contribution lies in the systematic assessment of model behaviour, spatial error patterns, and area estimates under strict spatial separation of training and testing data.

Land Use/Land Cover (LULC) classification is essential for understanding the spatial distribution of Earth’s surface and for supporting sustainable environmental and economic development. Recent extreme events in Central Europe have emphasized the link between LULC change and disaster vulnerability, highlighting the need for accurate monitoring. Advances in satellite technologies, particularly Sentinel-1 and Sentinel-2, combined with deep learning (DL) methods, have significantly improved LULC mapping. Convolutional Neural Networks (CNNs) excel at spatial feature extraction, Long Short-Term Memory (LSTM) networks capture temporal dependencies, and Convolutional LSTM (ConvLSTM) models integrate both spatial and temporal information.

This study evaluates the comparative performance of DL architectures for LULC classification in the Harz Mountains, Central Germany—a region experiencing notable forest cover loss. We assess 2D-UNet using two temporal processing approaches, examine the effect of attention mechanisms in 3D-UNets, and explore multiple integrations of ConvLSTM layers within UNet architectures. Our goal is to identify the most effective strategy for capturing spatio-temporal dynamics in LULC datasets, contributing to improved monitoring and management of vulnerable landscapes.

Monitoring land-use and land-cover (LULC) dynamics in rapidly urbanizing regions is critical for sustainable environmental planning. Dynamic metropolitan areas with rapid urbanization, such as Istanbul, Türkiye, are experiencing significant land-cover changes, among which deforestation is one of the most critical. This study presents a Google Earth Engine (GEE)-based framework to monitor LULC changes in Istanbul from 2016 to 2025 by fusing Sentinel-2 optical imagery, Sentinel-1 SAR and topographic data. From these datasets, a feature set—including spectral bands, vegetation indices, SAR backscatter metrics, and topographic variables—was derived and used to train a Random Forest (RF) baseline model on 2016 Land Parcel Identification System (LPIS) reference data. The baseline model was then applied across the time series to assess its temporal transferability, overcoming the limitation of up-to-date ground-truth data. The baseline model achieved an overall accuracy of 72%, calculated using a validation dataset derived from the LPIS reference data. Feature importance analysis revealed that structural variables—particularly DEM and SAR metrics—were the primary contributors to the classification, used in combination with optical features. Time-series results indicate a cumulative decline of 231 km² in agriculture and 379 km² in forest cover during the study period, inversely corresponding to urban growth. The results of the study highlight that, although applying a single-year model without independent annual validation data causes certain uncertainties—arising from methods, sensors, or topography (e.g., misclassifications)—the proposed framework is highly practical for monitoring deforestation and urbanization trends in complex landscapes.

Accurate and efficient land-use mapping provides intuitive spatial information, which helps to rationalize the planning and deployment of land resources, and provides a basis for urban planning, agricultural development, environmental protection and other aspects. This study utilizes the Google Earth Engine platform and the Resnet-50 method to explore the spatial distribution of land use in Daxing District, Beijing in 2023, by combining point of interest (POI) data, nighttime light data, Sentinel-1 data, and Sentinel-2 data. The results of the study show that the accuracy of land use mapping using different data is different, and the accuracy of the Resnet-50 method is better than that of the Random Forest method. Making full use of the band features and index features of Sentienl-1 data and Sentinel-2 data, nighttime light data and POI data can improve the accuracy of land use mapping results. Among them, the land use mapping accuracy of the proposed method is the highest, with an OA of 88.11% and a Kappa coefficient of 0.83. Ranking the importance of different features found that VH band in January-March has the most important effect on the land use mapping results and the residential land in the POI data has the least important effect on the land use mapping results. This study provides a feasible reference program for efficiently and accurately obtaining land use mapping data for a large study area.

Reliable urban risk assessment requires accurate and up-to-date information on building characteristics, particularly construction year and height, which are often incomplete or unavailable in existing databases. This study presents a cloud-based methodology for the automatic estimation of these parameters using multispectral and very high-resolution Earth Observation (EO) data. The proposed approach integrates temporal analysis of multispectral satellite imagery (Sentinel-2 and Landsat) with photogrammetric processing of very high-resolution stereo imagery (Pléiades). Building construction year is estimated by detecting temporal changes in spectral indices using spline-based modeling and discrete-difference analysis, achieving an accuracy of better than ±3 years. Building height is derived from digital surface models generated from satellite stereo imagery, with a mean accuracy of less than 2 m relative to LiDAR reference data (~1.40 m). The methodology was implemented in a cloud computing environment (Google Earth Engine and Google Colab) and tested in the City of Zagreb, Croatia. Validation results show robust performance, with an F1-score of 0.819 for construction year estimation and strong agreement between EO-derived and LiDAR-based height values. The results demonstrate the potential of EO-based methods for scalable, reliable extraction of building information, thereby supporting improved urban risk assessment and decision-making.

Zhejiang Province is experiencing rapid land use/land cover (LULC) transitions driven by urban expansion, infrastructure development, and increasing environmental pressures. Understanding historical dynamics and future trajectories of these changes is essential for informed regional planning and ecological management. This study analyzes land use changes from 2000 to 2020 and forecasts future patterns for 2025 to 2040 by integrating multi-temporal land use data with key spatial drivers, including elevation, slope, aspect, Normalized Difference Vegetation Index (NDVI), and proximity to roads and built-up areas. Change detection results reveal substantial declines in croplands and green spaces alongside rapid urban expansion, particularly around Hangzhou and Shaoxing and along major transportation corridors, reflecting an early phase of accelerated urbanization from a relatively small baseline. Future land-use dynamics were simulated using a hybrid Convolutional Neural Network - Long Short Term Memory (CNN-LSTM)-Cellular Automata (CA)-Markov framework that captures complex spatiotemporal dependencies and neighbourhood interactions under physical and anthropogenic constraints. Model projections indicate a more moderate growth regime from 2025 to 2040, with urban land increasing by 1.7%, croplands decreasing by 2.2%, and modest gains in water bodies (1.9%) and forest cover (1.1%), suggesting landscape saturation and policy-influenced land management. Validation using the observed 2025 land use map demonstrates strong predictive performance, achieving an overall accuracy of 86% and a Kappa coefficient of 79%. The results provide spatially explicit insights to support balanced development and enhanced ecological resilience.

Land use/land cover change (LULCC) analysis is essential for understanding environmental transformation and guiding sustainable territorial planning. Remote sensing offers a valuable source of information for monitoring these changes, but the accuracy of thematic maps depends heavily on the classification algorithm applied. This study compares the performance of three widely used supervised Machine Learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN)) for LULC mapping in the Greater Guayaquil region, a tropical area with persistent cloud cover.

A mosaic of Landsat-9 images from 2023 was processed in Google Earth Engine, followed by the selection of representative training and validation samples. The algorithms were implemented in R Studio, and accuracy was evaluated through confusion matrices and external comparison with MapBiomas Ecuador.

Four LULC classes were defined: Forest, Crops, Vegetation-free areas (urban/bare soil), and Water. Results indicate that SVM achieved the highest performance, with 93% overall accuracy and a Kappa coefficient of 0.91, followed by RF (92%; κ = 0.89) and ANN (90%; κ = 0.86). SVM also showed the highest spatial agreement with MapBiomas (>90%). Discrepancies were concentrated in rapidly changing urban–agricultural boundaries.

The superior performance of SVM is attributed to its capacity to model non-linear class boundaries in complex tropical landscapes. Despite expectations that RF would perform best based on previous literature, SVM proved more effective for this specific AOI. The study confirms that Landsat-9 combined with supervised ML models, particularly SVM, offers a robust and cost-effective approach for environmental monitoring and land-use planning in data-limited regions.

This study analyzed the advance of deforestation associated with artisanal mining in the Tapajós Environmental Protection Area (APA), north of Jacareacanga, Pará State, Brazil, for 2017 and 2024. The National Institute for Space Research (INPE) monitors deforestation in the Amazon using remote sensing, and the Tapajós APA stands out among protected areas for high rates of mining-related deforestation. Sentinel-2A images were used to generate the Normalized Difference Vegetation Index (NDVI) and RGB composites, assigning the 2017 NDVI to the red channel and the 2024 NDVI to the green and blue channels. Auxiliary data, including active mining processes in 2024 and the hydrographic network, were integrated for analysis. This approach enabled the identification of two distinct spectral responses: (i) cyan areas corresponding to regions that were non-vegetated in 2017 but exhibited regenerated vegetation in 2024; and (ii) red areas corresponding to regions that were non-vegetated in 2024. The results enabled visualization of the spatial progression of deforestation, particularly along drainage networks and in relation to active mining areas, revealing a pronounced expansion associated with artisanal mining across multiple waterways, with an upstream progression consistent with alluvial gold and cassiterite deposits. The data corroborate deforestation alerts issued by the Real-Time Deforestation Detection System (DETER) and the Deforestation Alert System (SAD)/Imazon, indicating the continuity of artisanal mining pressure in the Tapajós APA. The methodology demonstrated efficiency in detecting environmental changes and can be replicated in other areas under mining pressure, contributing to territorial monitoring and environmental management

Wildfire activity in Canada has increased significantly in recent decades, shifting to larger, more frequent fires and the emergence of megafires (>10,000 ha) across various ecozones. These events typically exhibit complex spatial patterns of burn severity, including larger and more homogeneous patches of high severity. The burn severity patterns and their drivers in megafires remain unclear, in particular, across diverse ecozones. Remote sensing indices such as the Relativized Burn Ratio (RBR) provide an effective means of quantifying burn severity at large spatial scales. This study uses RBR to evaluate nine megafires (each >50,000 ha) representing the 95th percentile and above of fire size within varying ecozones between 2016 and 2022. These fires were used to develop two random forest models: one predicting RBR and another predicting the within-fire z-score of RBR. Within-fire standardization of RBR was conducted to see whether it alters the relative importance of environmental drivers. In the RBR model (OOB R² = 0.75), regional variables such as ecozone and fire ID, along with drought code, were dominant predictors. In contrast, the z-score model (OOB R² = 0.68) emphasized fuel characteristics, including biomass and canopy closure, with additional contributions from elevation and drought-related variables. These results suggest that broad regional and fire-regime controls exert a stronger influence on burn severity than local fuel conditions at the megafire scale. Standardizing burn severity within fires reduces this regional signal but does not improve predictive performance, highlighting the importance of accounting for regional variability in large-fire dynamics.

Urban Heat Islands (UHIs) represent one of the most pervasive manifestations of human-induced modification of the land surface. They arise from the replacement of natural surfaces with impervious materials, reduced evapotranspiration, waste heat emissions, and altered aerodynamic roughness, collectively causing cities to exhibit elevated temperatures relative to surrounding rural areas.

The Local Climate Zone (LCZ) framework introduced by Stewart & Oke (2012) provides a standardized, physically based classification system for describing urban and natural landscape types according to their surface structure, cover, and thermal properties. Unlike traditional land-use/land-cover schemes, LCZs are explicitly designed for urban climate studies and allow for consistent comparison of urban form, function, and thermal behaviour across cities worldwide.

While LCZ maps of Bologna already exist within the WUDAPT protocol, they are characterized by a declared not high level of accuracy. So, the present work aims to produce the first detailed and reliable LCZ thematic map for the Municipality of Bologna, using higher quality, remotely sensed, input data.

To assess the impact of multi and hyperspectral imagery on the classification results, a Sentinel-2 and a PRISMA image were considered for the study.

Overall, this study provides for a first time a detailed and accurate LCZ map for the Municipality of Bologna and confirms the value of combining UCPs with both multispectral and hyperspectral data.

This study was carried out within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005.

This study focuses on predicting the evolution of internal urban spatial features, a dimension often overlooked in research that prioritizes urban expansion. Using the Yangtze River Delta as the case study, the work integrates multimodal remote sensing data—including high-resolution optical imagery and SAR data—to capture detailed land-use patterns, structural textures, and functional–transportation relationships. These fused datasets support the Futureland model, which applies parallel computing and Generalized Logistic Regression to simulate future spatial configurations with high efficiency and accuracy.

The results suggest that from 2030 to 2050, major cities such as Shanghai, Suzhou, Wuxi, Changzhou, Jiaxing, and Hangzhou will develop into more connected and compact urban clusters. Transportation networks and functional areas are expected to evolve in general alignment, while localized deviations reflect the complexity of internal urban dynamics. Land-use types are projected to undergo new spatial combinations and reorganizations, indicating improved continuity and diversity within the urban structure.

By systematically revealing trends in land-use evolution, transportation–function coupling, and urban form transformation, this research provides a clearer understanding of future urban spatial development. The proposed predictive framework offers valuable guidance for urban planning, governance, and sustainable regional development.

Global shifts in vegetation patterns highlight the need for effective monitoring as climate conditions intensify. Remote sensing provides valuable tools for detecting stress across landscapes. This study examines whether thermal satellite and airborne data can detect early stress linked to temperature, drought, and fire history. Within Australia, eucalypt species are increasingly vulnerable to climate-driven canopy dieback. Prolonged drought and extreme temperatures increase the risk of dieback. As eucalypts regulate leaf temperature through transpiration, when water is limited, leaf temperatures rise and can be detected using thermal imagery. Therefore, our research questions are:

• Can temperature-related stress patterns be identified?

• Does fire history affect long-term stress?

• Can thermal changes indicate dieback events?

• Does topography shape canopy stress?

Study Area:

Scott Creek Conservation Park in South Australia contains diverse native vegetation and steep terrain. The canopy is dominated by two stringybark eucalypt species, with areas burnt in the 2021 fire and unburnt controls.

Data and Methods:

Thermal satellite data from 2019–2025, including land surface temperature and water stress indicators, were analysed alongside local climate records. Airborne hyperspectral, LiDAR, RGB, and thermal imagery (50 cm) were processed to derive canopy structure, topography, and thermal patterns. A supervised classification was used to assess canopy condition.

Preliminary results:

Indicate that after a fire, high and moderate levels of vegetation stress increased and persisted into the following year. Vegetation in fire-affected areas showed no significant improvement in WUE during the recovery period. suggesting that fire-affected vegetation remained physiologically stressed despite visible regrowth.

The study investigates the long-term environmental and social impacts of asbestos mining in the Troodos Mountains of Cyprus, where chrysotile extraction between 1907 and 1988 left a lasting legacy of contamination and landscape degradation. Using multi-temporal aerial photographs, Sentinel-2 satellite data, and field observations, the research analyses land use transformations and vegetation recovery processes in the Amiandos mine area. A land use transfer matrix and Normalized Difference Vegetation Index (NDVI) were applied to assess ecological regeneration and detect spatial patterns of recovery.

To address ongoing environmental health risks, pre-trained deep learning models based on convolutional neural networks (CNNs) were used to identify asbestos-cement roofing in high-resolution aerial imagery. The results indicate measurable reforestation since the 1990s, but also reveal remaining asbestos waste deposits and deteriorated roofing materials posing persistent hazards to local communities.

The integration of remote sensing, vegetation indices, and deep learning methods provides a comprehensive approach to understanding environmental inequality in post-industrial landscapes. This framework supports the development of inclusive and data-driven restoration strategies consistent with the European Union’s environmental health goals. By combining spatial intelligence with machine learning, the study demonstrates the potential of remote sensing to monitor ecological recovery and mitigate asbestos-related risks in Cyprus and similar post-mining environments.

Urban heat and fine particulate matter (PM₂.₅) pollution are critical challenges for sustainable cities, but their high-resolution spatial and temporal patterns are not well understood. This study develops a multi-year 250 m downscaling framework to map the synergy between land surface temperature (LST) and PM₂.₅ in Wuhan, China. Using machine learning–based residual correction, annual, summer, and winter mean PM₂.₅ concentrations in 2015 and 2020 were downscaled from 1 km TAP data to 250 m grids. Correlation and spatial autocorrelation analyses were applied to reveal the spatial patterns of LST–PM₂.₅ interactions. The downscaled PM₂.₅ achieved high accuracy (R² > 0.80), and the heat–pollution relationship showed strong spatial heterogeneity. From 2015 to 2020, synergistic zones changed in the Urban area, consistent with the growth of impervious surfaces. These results provide a fine-scale spatial basis for understanding the coupled dynamics of urban heat and air pollution, supporting integrated strategies for climate adaptation and urban air quality management.

The coastal waters of Shanghai, situated at the confluence of the Yangtze River Estuary and the northern Hangzhou Bay, form a typical high-turbidity aquatic environment influenced by sediment discharge from the Yangtze River and strong tidal dynamics. Extensive urbanization and coastal development have led to the proliferation of hardened shoreline structures in this region, which have altered natural hydrodynamic conditions and sediment transport patterns, contributing to ecological issues such as wetland degradation. In recent years, Shanghai has initiated ecological restoration projects aimed at rehabilitating healthy coastal ecosystems. These restoration efforts, involving geomorphic reshaping, may directly disturb and modify sedimentary environments. However, their impact on suspended sediment concentration (SSC)—a key environmental parameter—across large spatiotemporal scales remains unclear. Traditional in-situ monitoring methods are inadequate for capturing such large-scale dynamic variations, whereas satellite remote sensing provides an effective alternative. This study utilizes multi-source remote sensing data to develop an inversion model for SSC suitable for Shanghai's coastal waters, systematically analyzing the influence of different shoreline types on sediment distribution. The findings illustrate how ecological restructuring of shorelines affects the spatial and temporal variations of SSC, thereby providing a scientific basis for optimizing coastal management strategies and assessing the effectiveness of ecological restoration efforts.

This study investigates the critical role of Urban Morphological Blocks (UMBs) in shaping Land Surface Temperature (LST) patterns across seasons and cities. Through a comparative analysis of Beijing, Wuhan, and Fuzhou, China, we integrated multi-source remote sensing and 3D building data to define UMBs based on building height and density. Employing robust statistical models (Geographical Detector and Random Forest Regression), we quantified the driving forces behind LST variations. Our results consistently identified Low-Rise, High-Density blocks as the primary heat contributors, while High-Rise blocks exhibited cooling effects. Crucially, we found a strong seasonality in dominant drivers: surface biophysical parameters (e.g., vegetation, impervious surfaces) governed LST in warm seasons, whereas 3D architectural morphology (especially building height) became paramount in winter. Furthermore, factor interactions revealed synergistic effects, with the combination of block type and vegetation yielding the highest explanatory power. These findings underscore the UMB as a vital unit for urban climate analysis. The study provides actionable insights for planners, recommending targeted mitigation in high-risk blocks, promotion of thermally efficient building forms, and the adoption of season-specific strategies to enhance urban resilience against heat stress.

Accurate, long-term precipitation data is essential for reliable drought monitoring. This study addresses heterogeneous uncertainties in existing precipitation datasets across mainland China by developing two modifiable weighting schemes: a Cheng-Kling-Gupta Efficiency weighted-ensemble model (CWEM) and Bayesian Model Averaging (BMA). These methods were used to merge seven monthly precipitation datasets into new weighted products (BMAEP and CWEP). The precision and drought monitoring utility of these fused products were evaluated against the benchmark Multi-Source Weighted-Ensemble Precipitation (MSWEP) product using gauge data. Results show that the new weighted schemes outperform individual datasets and MSWEP. Specifically, BMAEP-2P achieved a superior composite CKGE index of 0.828, CWEP-4P attained a higher correlation coefficient (CC) of 0.905, and CWEP-2P excelled in relative bias (0.579%) and root mean square error (20.755 mm). Furthermore, BMAEP or CWEP performed optimally for drought monitoring across all sub-regions of China at multiple time scales (1-24 months), with average highest CC and probability of detection values reaching 0.919 and 0.844, respectively. Contribution analysis identified CPC as the dominant factor enhancing model performance. The study demonstrates that CWEM and BMA methods effectively generate superior precipitation datasets for drought monitoring applications.

Fine particulate matter (PM₂.₅) poses serious risks to environmental quality, human health, and sustainable development. However, existing studies seldom achieve long-term, pixel-level PM₂.₅ projections or comprehensively evaluate scenario-based predictions under different development pathways. This study proposes an integrated method for projecting PM₂.₅ distribution at a pixel-level under multiple scenarios by incorporating land-use simulations, land surface indices, and spatial dependence effects. Using multi-temporal Landsat series, ground-based PM₂.₅ observations, and socio-economic data, we generated land-use projections under the Shared Socioeconomic Pathways (SSPs) using the Futureland model. Corresponding land surface indices (NDVI, NDBI, NDWI) were derived and used within a spatial lag model to predict PM₂.₅ concentrations for the Yangtze River Delta (YRD) from 2010 to 2030. Results indicate that under SSP1, characterized by sustainability, forest area and NDVI increase, leading to a significant decline in PM₂.₅ levels. Conversely, under the fossil-fueled SSP5 scenario, urban expansion drives up NDBI and PM₂.₅ concentrations. These findings demonstrate that increased green space and reduced fossil fuel reliance are crucial for improving air quality. The proposed method provides decision-makers with actionable insights for policy formulation and highlights the environmental importance of land-use planning. Future work will integrate dynamic meteorological models and conduct uncertainty assessments. This scenario-based projection framework can be applied to support sustainable urban and environmental management in rapidly developing regions.

The upper troposphere and lower stratosphere (UTLS) plays a vital role in the global climate system. Central China, particularly Hubei Province, is located directly downstream of the Tibetan Plateau, making it a key region for observing long-range aerosol transport. In this study, the vertical structure and transport mechanisms of UTLS aerosols over Hubei are investigated using a multi-year (2016-2018) satellite dataset. We applied rigorous Cloud-Aerosol Discrimination (CAD) scores and quality control procedures to nighttime CALIOP profiles to minimize cirrus cloud contamination. Our results show that the UTLS background over this region exhibits low aerosol loading during non-monsoon seasons. However, a substantial aerosol enhancement occurs in summer. The monthly mean extinction coefficient at 14-16 km reaches a peak of 8.05 × 10-3 km-1 in August, with a particulate depolarization ratio of ~0.25, indicating the presence of non-spherical particles related to the Asian Tropopause Aerosol Layer (ATAL). To investigate the physical drivers of this seasonal variation, we integrated MERRA-2 meteorological fields and HYSPLIT backward trajectories. The analysis reveals a dual transport mechanism: First, intense local deep convection driven by the East Asian Summer Monsoon (EASM) vertically pumps boundary-layer pollutants into the upper atmosphere. Second, the Asian Summer Monsoon Anticyclone (ASMA) and the westerly jet stream advect aged aerosols horizontally from the Tibetan Plateau to Central China. These findings provide direct observational evidence of how regional monsoon systems synergistically modulate stratospheric aerosol loading.

All living organisms significantly impact air quality, which is vital for the Earth's ecosystems. Air pollution has increased in the Indian subcontinent, mainly due to harmful gases and particles from industrialization and urban development. CNN Health reported that as of February 25, 2020, 21 out of the 30 cities with the worst air quality globally are in this region, with six in the top ten. Urgent research and pollution control measures are needed, especially in urban areas where human health and the environment are most affected.

The Air Quality Index (AQI) measures pollution levels from key pollutants like PM10, PM2.5, ozone, sulfur dioxide, and others, using a scale from 0 to 500. While pollution control boards collect ground data, the limited number of sensors in large cities can be a challenge. Satellite imagery enhances coverage, although in situ data remains essential in many areas.

This research aims to connect satellite remote sensing with air quality monitoring by determining air quality indices for pollutants in Dobson units. In situ sensors measure concentrations in micrograms per cubic meter, while satellites use molecules per square meter (Dobson units). Different techniques, like regression analysis, are used to develop location-specific indices for urban and suburban areas.

The focus of this research is on methodology rather than final conclusions, highlighting the importance of accurate real-world representations through reliable data and atmospheric models.

Air pollution remains a major environmental challenge in Kathmandu District, driven by rapid urbanization, increasing emissions, and meteorological influences. This study examines the spatial and temporal variability of PM₂.₅ and PM₁₀ from 2019 to 2024 by integrating satellite-derived Aerosol Optical Depth (AOD), ground-based measurements, and advanced statistical and machine learning techniques. Two regression approaches—a simple linear model using AOD and a multivariate model incorporating temperature, relative humidity, wind speed, wind direction, and planetary boundary layer height (BLH)—were evaluated using R² and RMSE metrics. The multivariate model consistently outperformed the simple linear regression, demonstrating improved predictive capability and was validated using PM data from the US Embassy monitoring station at Phora Durbar. Seasonal analysis showed pronounced pollution peaks in winter, with PM₂.₅ levels ranging from approximately 165–167 µg/m³, while summer exhibited the lowest concentrations (~51 µg/m³). PM₁₀ showed moderate seasonal variability with a notable decline during spring. The study also identified the influence of wildfire events and meteorological conditions on episodic pollution spikes. Despite limitations related to satellite resolution and uneven ground monitoring coverage, the integration of remote sensing, meteorological parameters, and machine learning proved effective for estimating particulate matter concentrations. Overall, the results highlight distinct seasonal pollution patterns and underscore the value of combined observational and modeling approaches for improving air quality assessment in Kathmandu District.

This study aims to systematically investigate the influence of spatial extent and spatial resolution on the estimation of soybean LAI and AGB and model transferability during the peak of the growing season. The research objectives are to: 1) assess and compare the predictive performance of Stepwise Multiple Linear Regression (SMLR) and Random Forest (RF) models for estimating LAI and AGB across different spatial extents and spatial resolutions; 2) evaluate the transferability of these models across spatial extents and resolutions to determine their robustness under varying scale conditions. Our results demonstrate that RF model outperformed SMLR and presented the highest LAI estimation accuracies across the three nested spatial extents with RMSE of 0.52m2/m2, 0.33m2/m2, and 0.31m2/m2, respectively, explaining 86%, 91%, and 91% of LAI variability at 1m2, 25m2, and 100m2 extents, respectively. Similarly, the RF model had the overall best accuracies with RMSE of 67.13g/m2, 76.98g/m2, and 58.03g/m2, respectively, explaining 83%, 86%, and 84% of soybean AGB variability at 1m2, 25m2, and 100m2 extents, respectively. Moreover, the results showed that the accuracies of both models increased for both LAI and AGB estimation at larger scales. We found that RF models outperformed SMLR in estimating soybean LAI and AGB at 3m resolution (LAI: R2=0.86, RMSE=0.39m2/m2, rRMSE=6.19%; AGB: R2=0.82, RMSE=59.09g/m2, rRMSE=18.09%) and 10m resolution (LAI: R2=0.92, RMSE=0.28m2/m2, rRMSE=4.36%; AGB: R2=0.80, RMSE=59.95 g/m2, rRMSE=18.35%), respectively. Further, the transferability of RF models showed weaker performance when applied to estimate soybean LAI and AGB at higher (or smaller) spatial extents and coarser (or finer) image resolutions.

This study looks to use fine resolution Synthetic Aperture Radar (SAR) for crop classification of small scale fields. The study site is the University of Guelph's Elora Research Station and looks to conduct crop classification with a random forest on four plots divided into 28 fields of 7 x 14 m in size. Four crop types are planted in each field which include, alfalfa, corn, soybeans, and winter wheat. The datasets used for the analysis are 4 SAR scenes taken during the May to July growing season with two of the plots used as training sets, and the other two as testing. The dataset is provided by the Finnish microsatellite company, ICEYE, with the data products being 0.5-meter resolution VV images. Additional textural information known as Grey Level Co-occurrence Matrix (GLCM) are processed from the SAR scenes and added to the random forest. The analysis was conduced at the pixel level and a 70-30 training and test split is used, with the final output map being aggregated to display the most populated classes present in each separate field. Results of the study show that only 6 out of 56 fields were wrongly classified. Corn had a producer accuracy (PA) of 0.93 and a user accuracy (UA) of 0.97, and oats with a PA of 0.85 and a UA of 0.88. Soybeans had a moderate performance with a PA of 0.87 and a UA of 0.63, and alfalfa performed the worst with a PA of 0.54 and a UA of 0.88.

The study of marine macrophytes is becoming increasingly important due to the threat of climate change to intertidal environments and the potential of macrophytes as nature-based climate solutions. Laminaria digitata (finger kelp), Saccharina latissima (sugar kelp), and Zostera marina (eelgrass) are three marine macrophytes whose habitats are known as blue carbon ecosystems due to their outstanding carbon sequestration capabilities. These species are found throughout the Eastern Shore Islands, Nova Scotia, an Area of Interest (AOI) for ecological and biological importance identified by Fisheries and Oceans Canada. Hyperspectral satellite imagery has been little explored as a solution to mapping marine macrophytes in comparison to other remote sensing data, including multispectral imagery and airborne hyperspectral imagery. To test the efficacy of hyperspectral satellite imagery for mapping marine macrophytes in cold temperate regions, we mapped finger kelp, sugar kelp, and eelgrass using a PRISMA image, near Sheet Harbour, NS, within the Eastern Shore Islands AOI. The results show that machine learning classifiers can use hyperspectral imagery to differentiate marine macrophytes, but it is more challenging to differentiate between species with very similar reflectance spectra, such as finger kelp and sugar kelp. The classification accuracy also decreases at deeper depths, where the benthos-reflected signal is diminished. Further investigation is needed to determine the value of narrow hyperspectral bands for species level mapping; initial results suggest that hyperspectral imagery can achieve improved discrimination of spectrally similar species of submerged aquatic vegetation compared to multispectral imagery of the same spatial resolution.

Forest damage is an increasing global concern, particularly as climate change intensifies the frequency and severity of both abiotic and biotic stressors. Early detection of stress-induced damage is essential for effective forest management, yet conventional methods remain labour-intensive and slow. A significant knowledge gap persists regarding how abiotic stress, such as drought, interacts with latent fungal pathogens that can shift from asymptomatic to aggressive under unfavourable conditions.

Multispectral imaging has demonstrated strong potential for detecting physiological disturbances in tree canopies, including pest outbreaks, but its capacity to identify pathogen-specific damage remains poorly explored.

In this study, we investigate whether UAV multispectral drone imagery can detect canopy damage linked to fungal pathogens in Norway spruce (Picea abies). Research was conducted in two contrasting trials in southern Sweden, representing optimal versus drought-prone growth conditions. Across the 2023–2024 growing sea-sons, tree vitality, needle condition, and phenology were monitored and paired with fungal community data to classify reference trees by pathogen type and stress response.

Weekly drone flights provided multispectral imagery that was radiometrically corrected, canopy-segmented, and processed to derive vegetation indices and individual-tree crowns. Using reference trees as training data, statistical models will assess damage patterns and vitality loss.

We expect to detect and distinguish stress signatures arising from combined biotic–abiotic interactions. And validate the Eurich et al. damage model in older trees.

Early- and in-season crop mapping provides vital information for precision agriculture. It is still a challenge for early- and in-season crop mapping because of the limited available images and similar spectral information. This study aims to enhance early- and in-season crop mapping by developing a Genetic Programming (GP) method to construct customized crop features. GP automatically generated candidate features for the target-crop using early- or in-season images, selected programs with substantial value disparities between target and non-target crops through the fitness function, and finally outputted the customized feature after the evolutionary process. These customized features were then compared with commonly used spectral bands and vegetation indices to evaluate their effectiveness for early- and in-season crop mapping. The results proved that the customized crop features had significant advantages in both early- and in-season crop mapping. The early-season accuracy in April after crop planting was 3.97% to 9.53% higher than spectral features and vegetation indices. Based on the classification for the in-season crop mapping, the customized crop features maintained the best performance. Advantages of customized crop features include the ability to automatically select effective bands of useful months without requiring expert knowledge, the ability to catch and enlarge the subtle spectral differences with the early- and in-season images, and the little information redundancy compared with spectral features and vegetation indices. It can be concluded that the customized crop features are outstanding for early- and in-season crop mapping.

Efficient nitrogen (N) management is a key factor for sustainable potato production, as over- or under-fertilization can significantly affect yield, quality, and environmental outcomes. This study explores the potential of unmanned aerial vehicle (UAV) multispectral imagery and machine learning (ML) to predict in-season potato nitrogen status under field conditions in Manitoba, Canada. A DJI Mavic 3M equipped with four spectral bands (green, red, red-edge, and near-infrared) was used to capture canopy reflectance at 15 m altitude during the 2023 and 2024 growing seasons. Vegetation indices (VIs) such as NDVI, GNDVI, CIgreen, TCARI, and SRRE were extracted from orthomosaics and evaluated for their relationships with petiole nitrogen concentration (PNC). Feature selection methods including Recursive Feature Elimination (RFE), Boruta, and Partial Least Squares Regression (PLSR) were applied to enhance model efficiency. Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR) algorithms were compared for prediction accuracy. RF combined with RFE achieved the highest performance (R² = 0.57), confirming its robustness to multicollinearity and nonlinear relationships. The results highlight the strong relevance of CIgreen and red-edge indices to N variability and demonstrate the potential of UAV-based spectral sensing integrated with ML for precision nitrogen management in potato systems.

Satellite remote sensing remains central to global mangrove forest mapping, yet the effectiveness of existing products is often limited by coarse spatial resolution and insufficient locally representative training data. These constraints are particularly evident in many African coastal regions, where access to very high-resolution satellite imagery and field observations is scarce. Deep learning–based super-resolution offers a promising alternative by enhancing the effective resolution of freely available imagery. This study evaluates the utility of applying semantic segmentation to super-resolved Sentinel-2 and Sentinel-1 data for mangrove mapping in two ecologically distinct regions: Tanzania and Ghana. Using analysis-ready data from Digital Earth Africa, temporal median composites of Sentinel-2 VNIR, red-edge, and SWIR bands, together with Sentinel-1 VH and VV images, were generated at 10 m resolution. A modified ESRGAN model was trained to produce imagery with a five-fold increase in spatial resolution. Both the original and super-resolved datasets were used to train a U-Net–based binary segmentation model, supported by training labels derived from Global Mangrove Watch data, Google Earth imagery, drone surveys, and fieldwork.

Results indicate that super-resolved imagery substantially improves the accuracy and precision of mangrove classifications relative to the original-resolution images. The enhanced spatial detail supports the detection of small mangrove patches, complex shoreline features, and local degradation patterns, yielding more complete estimates of mangrove extent. Incorporating Sentinel-1 backscatter further improves mapping accuracy. The study demonstrates that deep learning–based super-resolution can overcome key limitations of open-access satellite data, enabling more reliable, fine-scale mangrove mapping.

Operational quantification of forest aboveground biomass remains one of the most demanding components of Verified Carbon Standard (VCS) project monitoring, largely due to the need for repeated large-scale field inventories. To reduce costs and enable automated updates, CERFO developed for Ecotierra a hierarchical modelling system integrating field measurements, drone photogrammetry, and Sentinel-1/2 imagery. The system is designed to generate biomass updates autonomously every 1–3 years.

In 2024, seventy-two field plots were installed, and plot-level biomass was computed using regional allometric equations. Drone acquisitions from RGB and multispectral sensors produced high-resolution structural and spectral predictors (>300 variables). A machine learning ensemble (AutoGluon), trained on a stratified split of 68 plots, achieved strong accuracy (R² = 78.5%, relative bias = 1.7%). Bias-corrected drone predictions (via empirical quantile mapping) were then used as pseudo-observations for the satellite modelling stage. The Sentinel-based model, combining optical and radar indices, reached R² = 67.8% with a relative bias of 0.79%, demonstrating the value of multi-sensor integration.

A comprehensive uncertainty analysis using one million Monte Carlo simulations confirmed the stability of aggregated results (T² = 84.2%, mean bias = –0.22%), meeting MRV requirements for carbon reporting. The final operational product is a fully automated processing chain that retrieves satellite images, performs preprocessing, computes predictors, applies the trained model, and exports biomass maps.

This approach provides a robust and scalable solution for continuous biomass mapping and forest carbon monitoring. Future improvements include expanding field sampling across broader ecological gradients to reduce uncertainty in underrepresented environments and further strengthen model generalization.

Accurate estimation of aboveground biomass (AGB) is critical for understanding carbon dynamics and forest structure at regional and global scales. Waveform LiDAR, with its ability to capture detailed vertical profiles of vegetation, has proven highly effective for AGB estimation. However, spaceborne waveform LiDAR missions such as NASA’s GEDI face limitations due to sparse sampling, necessitating integration with complementary remote sensing datasets for continuous coverage. This study develops a comprehensive framework to evaluate the contribution of multispectral optical imagery (Sentinel-2) and dual-polarized SAR data (Sentinel-1 C-band and ALOS PALSAR L-band) in reconstructing forest structure across multiple canopy layers in a tropical forest in French Guiana. Using LVIS waveform LiDAR as a reference and an AGB map derived from LiDAR–SAR fusion, Random Forest models were trained to predict LiDAR waveform metrics at relative heights (RH10–RH98), followed by SHAP analysis to quantify feature importance. Results reveal that satellite data exhibit greatest sensitivity at mid-canopy levels (RH55–RH85), with SWIR bands outperforming other optical features, particularly during the dry season when canopy moisture is reduced. SAR features, especially cross-polarized channels, provide consistent contributions across biomass ranges, though their effectiveness declines in very dense forests (>350 Mg/ha).

Forest structural complexity is a key component of forest ecosystems and generally reflects the combined characteristics of tree height, diameter at breast height (DBH), canopy cover, tree spacing, and species composition. While spaceborne LiDAR systems such as GEDI provide near-global full-waveform observations for deriving the Waveform Structural Complexity Index (WSCI), the discrete distribution of GEDI footprints limits their spatial continuity. This study addresses this challenge by integrating GEDI-derived WSCI samples with multisource remote sensing data to enable large-scale mapping of forest structural complexity. We developed and compared machine learning models (RF, SVR) and a deep learning architecture (ConvNeXt) to evaluate their ability to predict WSCI from multisource remote sensing data. The results show that the deep learning framework, supported by multisource remote sensing data, effectively overcomes the discrete footprint limitation of GEDI and enables spatially continuous mapping of forest structural complexity at the regional scale. The ConvNeXt model demonstrated clear advantages, reducing RMSE and MAE to 0.55 and 0.43 (compared with 0.60/0.49 for RF) and improving IoA and correlation to 0.73 and 0.61, thereby enhancing the reliability of regional-scale complexity mapping despite the sparse GEDI footprint distribution. This provides a practical and scalable pathway for large-scale forest structure characterization and supports regional forest monitoring and management. Future work will include expanding the study area, incorporating additional field measurements, integrating terrain-related variables for improved modeling under complex topography, and exploring multi-year datasets to assess temporal dynamics in forest structural complexity.

By integrating remote sensing observations, topography, and crop growth parameters, a multimodal deep learning model named Soil Spectral–Spatial Cross-Attention Network (Soil-SSNet) is proposed. Soil-SSNet includes a spectral sequence convolution module to capture dynamic spectral features, a spatial attention module to address surface heterogeneity and uncover the response relationship between salinity and natural environmental variables, and a multi-head cross-attention mechanism that uses spatial features as Query to guide the selection of spectral-index responses. Compared to traditional machine learning models such as Random Forest (RF) and Support Vector Regression (SVR), the overall accuracy of Soil-SSNet improves by approximately 40%. After incorporating multi-source covariates (water conditions, crop growth status, and topographic factors), the model’s accuracy further increases by about 25%. With the addition of the cross-attention mechanism, accuracy improves by another 35%, significantly enhancing the fusion capability of spectral and environmental information and achieving soil salinity inversion with higher accuracy and stronger generalization. Finally, spectral sensitivity analysis reveals that the 705–750 nm and 1580–2350 nm bands contribute the most to salinity inversion. Mechanism analysis further uncovers a significant coupling effect among salinity, crop growth, and topography: vegetation growth characteristics reflect the intensity of salt stress, topographic factors dominate the redistribution pattern of water and salt, and soil moisture dynamics determine the accumulation and dispersion patterns of salinity. In summary, Soil-SSNet not only improves the accuracy and interpretability of soil salinity inversion in saline-alkali farmland but also provides quantitative evidence for understanding the environmental processes and mechanisms of salinization.

Tanzania hosts one of Africa’s largest Protected Areas (PA), yet deforestation remains widespread in surrounding unprotected landscapes. Assessing the effectiveness of PAs requires analytical approaches that account for environmental and accessibility biases inherent in PA placement. This study presents a remote-sensing-based spatial modelling workflow that integrates Global Forest Change (GFC) forest-loss time series (2012–2022) with terrain, accessibility, and demographic covariates to quantify avoided deforestation attributable to protection.

Biophysical and anthropogenic variables influencing forest-cover change, including elevation, slope, distance to roads, settlement density, and population distribution, were harmonised to a 30 m grid and combined with protected area boundaries from the World Database on Protected Areas. To address spatial biases, Propensity Score Matching (PSM) was applied to match protected forest pixels with statistically similar unprotected pixels, reducing confounding effects and enabling a credible counterfactual baseline. A binomial logistic regression model was then fitted to the matched dataset to estimate the likelihood of deforestation under different conservation categories.

Results show that protected forests were, on average, about three times more likely to avoid deforestation than comparable unprotected forests. National Parks and Game Reserves demonstrated the strongest outcomes, being nearly ten times more effective at avoiding deforestation. Nature Forest Reserves were around three times more effective, while Forest Reserves and Game Controlled Areas showed more modest effects, being roughly twice as likely to avoid deforestation.

The analysis is transparent, reproducible, and scalable, demonstrating how Earth observation and spatial causal inference can strengthen national forest monitoring, support conservation planning, and inform policy processes.

Climate change is increasingly disrupting agricultural ecosystems, particularly in Canada, where rising temperatures and altered precipitation patterns are impacting crop resilience. Alfalfa (Medicago sativa L.), a key forage crop valued for its productivity and nutritional quality, is especially vulnerable to winter stress due to reduced cold tolerance and increased damage from thaw cycles. This study presents a remote sensing-based framework for estimating fractional vegetation cover (FVC), a critical indicator of crop health and ecosystem stability. By integrating Sentinel-2 satellite imagery with high-resolution UAV data, the approach leverages machine learning algorithms, including random forest (RF) and gradient boosting (GB), to efficiently predict alfalfa FVC. UAV-derived RGB orthoimages provide detailed spatial reference data, minimizing the need for extensive field surveys. The proposed method demonstrates the potential of combining multi-source remote sensing with ML to capture complex vegetation dynamics and improve monitoring accuracy. Although both models showed high potential in estimating the alfalfa FVC, RF outperformed GB in terms of all evaluation criteria. The resulting FVC maps provide actionable insights for early spring field assessments, enabling the timely identification of damaged areas and supporting informed crop rotation decisions. The proposed framework was tested across multiple alfalfa fields in 4 provinces of Canada, including Quebec, Ontario, Nova Scotia, and Manitoba, demonstrating robust performance under varying environmental conditions. Its scalability and adaptability make it suitable for broader applications in precision agriculture and climate-resilient crop monitoring.

The Chambal–Yamuna Badland Zone (CYBZ) is among the most severely degraded semi-arid landscapes in India, where persistent gully development reduces soil productivity and threatens long-term environmental stability. This study examines how vegetation dynamics and surface deformation have evolved in the CYBZ over the past 25 years by integrating long-term optical and SAR-based remote sensing observations. MODIS NDVI data (2000–2024) processed through Google Earth Engine were used to track vegetation greening and browning patterns, while Sentinel-1A/B SAR datasets (2017–2024) were analysed using Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) approach to quantify deformation linked to active erosion.

The NDVI time series shows a clear and statistically significant rise in vegetation cover across the region, with the strongest greening occurring in the eastern badlands, particularly after 2015. This widespread improvement aligns with increasing rainfall and indicates a gradual transition from highly eroded terrain to more vegetated and potentially stabilised surfaces. InSAR results reveal minimal ground deformation within major gullies, suggesting that gully erosion during the study period has been low. Seasonal fluctuations observed in PS displacement curves correspond to vegetation cycles rather than ongoing surface lowering. Negative deformation signals are predominantly associated with agricultural zones adjacent to the badlands.

Overall, the combined use of MODIS NDVI and Sentinel-1 InSAR provides a robust framework for monitoring ecological recovery and erosion dynamics in geomorphically fragile landscapes. The findings highlight increasing vegetation stability and reduced gully activity, offering new insights into the contemporary evolution of the Chambal badlands