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.