Conference Agenda

The presence of refraction-induced systematic errors has always been a cause of concern in the field of underwater photogrammetry.

This work extends previous studies from the authors with new simulations specifically aimed at practical applications underwater using popular sensor devices and configurations, such as GoPro action cameras fitted with standard flat port housing that are very common among marine ecologists and archaeologists.

We aim at investigating whether approaches used by regular photogrammetry above water can be applied underwater without a significant accuracy loss for the application of interest.

Due to the complexity of collecting ground truth data, simulations are used. We utilize the POSER framework (https://github.com/GEOSS-UNISS/POSER) developed within the 2024 ISPRS Education and Capacity Building Initiatives (ECBI).

We investigate the benefits and cons of the refractive vs the implicit modelling approaches with respect to estimability of camera calibration (refractive) parameters, need for pre-calibration setups with approaches from literature, availability of ground control points, and assessing the accuracy of both approaches against ground truth simulated data.

The accuracy is reported as discrepancies in the reconstructed 3D models, exterior orientation and camera calibration parameters.

Reconstructing the water surface in refractive domains such as rivers and lakes is challenging, since light bending at the air-water interface alters the apparent geometry and breaks the straight-ray assumption of conventional image-based 3D reconstruction. Accurate water surface models are therefore a key prerequisite for many refraction-aware applications. This contribution investigates the potential of three passive image-based methods, Structure from Motion (SfM), Multi-View Stereo (MVS), and Monocular Depth Estimation (MDE), to derive a geometrically consistent water surface model from UAV imagery of the Pielach River study site in Austria. The dataset represents a demanding scenario with clear, fast-flowing water and low texture, which causes strong refraction and poor feature stability. Quantitative comparisons against LiDAR-derived reference surfaces show that SfM yields sparse and inconsistent points, MVS reconstructs the riverbed instead of the water surface, and MDE exhibits scale and offset inconsistencies despite explicit calibration using SfM reprojections. Completeness remains below 45 % for all methods with mean vertical deviations in the decimetre-to-metre range. The results indicate that current image-based approaches are insufficient for reliable water-surface reconstruction in such settings, reinforcing the need for an explicitly derived surface model as input to refraction-aware modeling, for example in bathymetric reconstruction and future refractive neural rendering methods, rather than relying on implicitly learned water surfaces.

This article studies the complementary usage of RTI and SfM-MVS visual inspection (visual testing) of welds under water. Two compact low-cost lighting domes of different designs were developed and deployed with a monochromatic camera at close range.

The lighting domes generate homogenous and direct illumination, respectively, tailoring the requirements of SfM and RTI. The camera is housed in a cylindrical tube, equipped with a dome port interface.

The 3D reconstruction in combination with RTI models could augment existing testing strategies and provide digital, gapless documentation. Experiments were conducted in laboratory in air, clear and turbid water questioning were the capabilities and limits are for given setup with respect to visual testing of welds.

Under the correct lightning, in air the techniques perform on a high accuracy level and are well suited for inspecting welds digital and interactively. Underwater the results differ in dependence of the degree of turbidity and prove to be sensitive for configurational parameters leaving space for improvements of acquisition and processing workflows. However, even in turbid water the 3D reconstructions and RTI models could be calculated enabling novel possibilities for weld inspection.

A novel hand–eye calibration method for ground-observing mobile robots is proposed. While cameras on mobile robots are common, they are rarely used for ground-observing measurement tasks. Laser trackers are increasingly used in robotics for precise localization. A referencing plate is designed to combine the two measurement modalities of laser-tracker 3D metrology and camera-based 2D imaging. It incorporates reflector nests for pose acquisition using a laser tracker and a camera calibration target that is observed by the robot-mounted camera. The procedure comprises estimating the plate pose, the plate–-camera pose, and the robot pose, followed by computing the robot–-camera transformation. Experiments indicate sub-millimeter repeatability.

Early detection of structural issues is crucial for timely maintenance and extending bridge lifespans. Conventional visual inspections alone are not sufficient for the vast number of structures, highlighting the need for intelligent, real-time monitoring systems. The studies presented here were conducted as part of the DFG priority programme SPP100+ in collaboration with the IDA-KI project. In addition to conventional civil engineering sensors, the 45-meter-long openLAB research bridge, a three-span prestressed concrete structure, was equipped with target fields for photogrammetric measurements. During controlled load tests, in which a motorized load vehicle passes over the bridge, a high-resolution camera captures image sequences of the measurement fields. The photogrammetric workflow involved camera calibration, image sequence acquisition, and precise 3D coordinate determination using coded targets. Displacement values in the range of several millimeters were calculated frame-by-frame. Results from a typical load cycle showed initial upward deflection followed by downward movement, corresponding to the load vehicle’s passage. This approach demonstrates the potential of photogrammetry for accurate, non-contact deformation monitoring, supporting the development of digital twins and advanced structural health monitoring systems for bridges.

Body donation remains critically important for anatomical science, allowing examination of biological structures with three-dimensional (3D) context. However, body donors (cadavers) are a time-limited resource and the scarcity of body donors has prompted an interest in digital body preservation. Multiple imaging techniques (e.g., conventional photogrammetry[CPG], stereophotogrammetry[SPG] and computed tomography[3DCT]) can capture the 3D characteristics of a specimen indefinitely. Digital anatomical records provide an opportunity to measure anatomical structures in the absence of the physical specimen. In 2022, the face of a preserved body donor was digitally reconstructed using CPG and 3DCT. 28 months later, a repeat survey was performed using SPG and a series of facial landmarks were directly measured. The accuracy and stability of facial soft-tissues over time were measured using point-to-point and cloud-to-mesh techniques. The results show that anatomical models produced by 3DCT and CPG produce similar facial measurements to those acquired by SPG and direct measurement at later timepoints. These data indicate that chemical fixation adequately stabilises facial anatomy over time, each sensor can be used interchangeably for facial measurement and models can be co-registered with minimal discrepancy.

Speech is a highly complex and multidimensional process, requiring precise coordination of muscular actions within the vocal tract. Disruptions or delays in speech motor control often lead to speech impairments. Recent advancements in markerless facial tracking technology enable the collection of objective measurements to assess these impairments. To obtain such photogrammetric measurements, a multi-camera network is employed, making accurate camera calibration essential. This paper examines the constraints applied during the calibration process. Two adjustment strategies were evaluated. The first, Independent Adjustment (IDP), performs self-calibration for each camera without introducing constraints. The second, Combined Adjustment (CMB), incorporates object space constraints by ensuring that object point locations observed from all cameras remain consistent. Given the cameras’ narrow fields of view, both IDP and CMB were tested with additional constraints related to the principal point offset. Each adjustment was executed under two conditions: fixing the principal point offset to zero or estimating it as part of the calibration. Results indicate that the choice of adjustment significantly affects the interior orientation parameters (IOPs). IDP with the principal point offset fixed to zero produced the most accurate outcomes. However, variations in IOPs had no meaningful impact on object space coordinates. These findings suggest that the simplest approach—IDP with the principal point offset fixed to zero—offers reliable calibration for multi-camera systems used in speech assessment. This streamlined method can be adopted in future applications to enhance efficiency without compromising accuracy.

Cultivated land conversion to built-up area is a core form of farmland non-agriculturalization and a major threat to farmland protection in China. Current remote sensing methods for detecting such changes face two limitations: insufficient integration of domain prior knowledge and the inability of purely data-driven models to achieve both high Precision and Recall. To address these issues, this study proposes a knowledge graph-enhanced change detection method. A multi-scale knowledge analysis framework incorporating feature, scene, and business knowledge layers is constructed to systematically integrate multi-source geographic information into structured semantic representations. A knowledge fusion residual network, KG-MS-ResNet, is designed based on ResNet-18 with modifications to the first convolutional layer for bi-temporal image inputs. TransE embeds geographic indicator knowledge into multi-scale semantic vectors, while a semantic–feature dual-path fusion strategy and a knowledge-guided attention mechanism enable deep coupling between image features and domain knowledge. Experiments in Pei County, Jiangsu Province, show that the proposed method outperforms baseline ResNet across all metrics, with Recall increasing by 4.84 percentage points and F1-score by 0.0752. The results demonstrate that integrating domain knowledge graphs with deep learning significantly improves detection performance, offering a semantically interpretable solution for monitoring cultivated land non-agriculturalization and advancing the integration of knowledge-driven and data-driven approaches in intelligent remote sensing interpretation.

Road change detection is essential for maintaining up-to-date digital maps; however, conventional update processes rely heavily on the manual interpretation of aerial imagery, leading to high labor costs and inconsistent outcomes. To address these limitations, this study proposes an automated road change detection method that integrates aerial orthophoto-based segmentation with geometric boundary deviation analysis. Road areas are first extracted from high-resolution aerial orthophotos using SegFormer, a Transformer based semantic segmentation model. The segmentation results are then converted into vector polygons for geometric analysis. Structural changes, such as newly constructed or removed roads, are detected through a difference-based comparison with historical digital maps. Simultaneously, shape changes are quantitatively analyzed by measuring geometric deviations between road boundaries.

Specifically, vertex-wise distances between corresponding boundaries are computed, and the overall deformation is evaluated using Root Mean Square Error (RMSE), incorporating Z-score-based outlier removal to ensure robustness against noise. Experimental results demonstrate that the proposed method effectively detects both structural changes and subtle geometric variations, including road expansions and boundary shifts. Furthermore, the method enables clear object-level classification of change types, providing a practical and efficient framework for digital map updating workflows.

Accurate building change detection depends on how well building boundaries are delineated, as distortions and merging errors hinder reliable correspondence. In dense urban areas, deep learning models frequently merge adjacent buildings—especially within narrow gaps—producing structural inconsistencies that lead to change detection errors. We propose a post-processing method integrating Local Rank-Based Prior Calibration, which reinterprets Softmax probabilities as percentile-based local ranks, with Graph-Cut refinement for structural correction. The refined mask is matched with historical building data to classify four change types. Experiments using aerial imagery from Seoul show that the method reduces structural errors, lowering under-segmentation from 51.64% to 22.02% and improving IoU from 0.748 to 0.759. In change detection, it increases the mean F1-score from 0.522 to 0.608 and improves all classes, including new construction, whose F1-score rises from 0.269 to 0.707. Ablation studies confirm that calibration and graph-based refinement both contribute to the improvements. These results show that stabilizing segmentation outputs enhances the reliability of building-level change detection in dense urban environments.

OpenStreetMap (OSM) is a representative open geospatial platform that provides free access to major spatial objects, including buildings worldwide, constructed through crowdsourcing-based manual digitization. However, subjective differences among contributors and the absence of unified quality control standards have led to the accumulation of positional offsets and boundary shape errors in building polygons. To address this issue, studies using deep learning-based semantic segmentation for OSM quality improvement have been conducted. Nevertheless, Transformer-based segmentation models exhibit an under-segmentation tendency that merges adjacent buildings into a single object, along with limitations in precise boundary delineation. To overcome these challenges, this study proposes a two-stage framework that integrates SegFormer, which excels in global context recognition, with SAM 2, which is capable of precise boundary segmentation. In the first stage, SegFormer semantically segments building regions from a true orthoimage, and in the second stage, SAM 2 infers object-level precise boundaries using the bounding boxes of OSM polygons as box prompts. The two results are combined into a prior probability map, enabling uncertain boundary regions to be re-evaluated in an unsupervised manner. In experiments conducted over the Suseo-dong area in Gangnam-gu, Seoul, the proposed method achieved a BIoU of 70.40%, an improvement of 23.85 percentage points over OSM building data, with consistent performance gains across all evaluation metrics. This framework offers scalability applicable to any region worldwide without additional label construction, provided that high-resolution true orthoimagery and OSM data are available.

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

This contribution presents a benchmarking study of a lightweight deep learning model for automatic pothole detection in asphalt pavements. Accurate and cost-effective identification of surface distresses is essential for road safety and for prioritising maintenance, especially in cities where traditional visual surveys are still predominant. We adapt and train a compact YOLO-based object detection architecture on a dataset of annotated street-level images, covering different lighting conditions, pavement textures and distress severities. The study evaluates how input resolution, confidence thresholds and data augmentation strategies affect detection performance and inference speed, and compares the lightweight model with heavier state-of-the-art detectors. Results indicate that it is possible to obtain competitive accuracy while maintaining real-time processing capabilities on modest hardware, which is crucial for deployment in mobile inspection platforms such as smartphones, dashcams or low-cost onboard units. The paper discusses opportunities and limitations of integrating deep learning into pavement management systems and outlines perspectives for extending the approach to other types of defects and to larger road networks.

This study presents a deep learning framework integrating Sentinel‑2, Sentinel‑1, and GEDI LiDAR to map Ethiopia’s canopy height at 10‑m resolution from 2019–2024. A shift‑aware loss function was employed to correct geolocation errors inherent in GEDI L2A footprints, and height‑weighted penalties addressed systematic underestimation in tall forests. Results show a national net gain of 23,537 km² in tree cover >8 m, reversing long‑standing deforestation trends. Gains concentrated in low‑to‑mid canopy strata (<20 m), strongly associated with major restoration interventions including the Green Legacy Initiative (GLI), REDD+, and the Sustainable Land Management Program (SLMP). Losses persist in western and southeastern highlands, driven by agricultural expansion, wildfires, infrastructure development, and large‑scale agricultural investments. This work demonstrates the operational value of multi‑sensor deep learning for near‑real‑time monitoring of restoration outcomes in data‑scarce regions.

Accurate tree-level analysis in forests via LiDAR scanning is essential for biomass estimation, canopy structure assessment, and carbon monitoring, yet remains constrained by the scarcity of large-scale annotated LiDAR datasets and the high cost of manual annotation. To address this, we present a novel approach that integrates 3D tree models with UAV-borne LiDAR simulation to generate synthetic forest point clouds with comprehensive annotations. Our approach generates diverse woodland, open, and closed forest structures, producing Synthetic Forest, a benchmark datasets of three 1 ha scenes containing 38–47 million points each, with densities of 3300–3860 points/m² and average spacing of 2 cm. Each scene contains between 70 and 216 individual trees, along with understory vegetation, deadwood, stumps, rocks, and bushes, all automatically annotated with semantic classification IDs, instance IDs, and tree IDs for volume estimation. The proposed pipeline provides automated, error-free ground truth for leaf-wood classification, instance segmentation, and wood volume estimation. We provide a guideline for generating forest plots and utilizing the datasets for diverse forestry tasks. By eliminating the need for costly field data collection, our pipeline offers scalable, customizable synthetic datasets that accelerate forest inventory. The Synthetic Forest dataset is publicly released via Zenodo (DOI: 10.5281/zenodo.17568131), enabling reproducible research and supporting further developments in forest monitoring and management.

Accurate mapping of tree species is essential for forest monitoring, biodiversity assessment, and ecological applications. Very high-resolution UAV imagery provides detailed structural and spectral information, but species-level segmentation remains challenging due to limited annotated data, complex crown geometries, and strong visual similarity among taxa. Recent Remote Sensing Foundation Models (RSFMs) offer new possibilities by providing transferable representations learned from large, multimodal geospatial datasets.

This contribution introduces a two-phase framework that combines foundation model initialization with multi-temporal UAV imagery to enhance fine-grained forest segmentation. In Phase 1, a DeepLabv3+ network is initialized using FoMo-Net, a ViT-based RSFM pre-trained on the multi-scale FoMo-Bench benchmark. This initialization enables strong generalization from heterogeneous global forest datasets to very high-resolution UAV scenes. In Phase 2, phenological information is integrated by fusing May and September UAV acquisitions through temporal difference composites and pseudo-label refinement, allowing the model to resolve species-specific seasonal patterns.

Experiments on the Québec Trees Dataset, covering 14 species at 0.02 m GSD, demonstrate substantial performance gains. Foundation model initialization improves overall accuracy from 52.79% to 71.21%, while incorporating multi-temporal cues further increases accuracy to 78.21%. The results highlight the complementary roles of structural priors learned by RSFMs and phenological information captured by UAV time series for detailed forest species mapping.

Accurate mapping of forest types and vegetation characteristics is essential for monitoring biodiversity and forest dynamics. Traditional Deep Learning (DL) models trained on Sentinel-2 time series achieve high performance, but require extensive preprocessing and sensor-related fine-tuning. In this study, we evaluate the recently introduced AlphaEarth Foundations (AEF) embeddings, which is global, multi-modal feature representation of the earths surface, for forest mapping in Italy. We compare a) a Multi-Layer Perceptron trained on AEF, b) a Time-Series Transformer trained on Sentinel-2 annual time series and CHELSA climate data, and c) a Cross-Attention fusion model combining both feature sets. Using 5-fold cross-validation in a regression and a classifaction task on two datasets (evergreen broad-leaved tree cover ETC, forest vegetation type FVT) we find that the combined model consistently outperforms the single-source approaches (RMSE = 0.161, Accuracy = 0.757). AEF-based models achieve comparable accuracy to the Sentinel-2-based model while reducing extensive time series preprocessing and training time by an order of magnitude. Feature attribution using integrated gradients reveals that AEF provides stable baseline representations, while Sentinel-2 inputs add phenology-related detail. The results show, that integrating generalized embeddings with specialized spectral-temporal features improves predictive performance for forest mapping.

In Japan, many urban park trees are becoming large and aged, increasing the risk of structural failures caused by extreme weather events and biological deterioration. Effective management therefore requires reliable risk assessment, for which accurate tree species identification is one of the fundamental prerequisites. However, species identification still depends heavily on visual assessment by skilled professionals, posing challenges in efficiency and objectivity. This problem is particularly significant for broad-leaved trees, which exhibit high species diversity and morphological variability. In addition, labor shortages have intensified the demand for automated and reliable classification techniques. This study proposes a high-accuracy classification method for broad-leaved tree species using ground-level images captured with a commercially available RGB camera and deep learning. The proposed method extracts small local patches that capture species-specific visual features, such as leaf shape and bark texture, commonly used by professional arborists for species identification. These local features are evaluated individually using deep learning models, allowing fine-scale visual characteristics to be effectively utilized for classification. To address variability in outdoor imaging conditions, including illumination changes, shadows cast by branches and leaves, and moss attachment, multiple patches are classified independently and the results are integrated through majority voting, improving classification robustness. Experiments were conducted on seven tree species commonly found in Japanese urban parks: cherry, ginkgo, zelkova, konara oak, sawtooth oak, plane tree, and flowering dogwood. The results demonstrate that the proposed method achieves a maximum classification accuracy of approximately 95% under real-world conditions, demonstrating its effectiveness for practical urban tree management.

This study compares visualization types (3D, 2D, Oblique 3D, and Combined 2D+3D, coupled in a pairwise fashion for different tasks and scenarios), investigates their influence on decision-making across selected urban planning tasks (Site selection, Scenario Selection), and Distance Estimation as a baseline task that we assumed is relevant in both. Our goal was to inform the participatory urban planning process. In a controlled user study with 40 participants, we evaluated whether visualization type affects decision outcomes and distance estimation, complemented by participants’ visualization preferences before and after the experiment. The results confirm the previously well documented evidence that participants are considerably more successful with distance estimation with 2D visualizations, and their decisions vary depending on the examined visualization and task types. We observe that different formats support different task requirements, i.e., each visualization type exhibits distinct strengths depending on the task. These findings indicate that visualization choice in urban planning should be adapted to the task and context rather than treated as an interchangeable artifact.

With the rising number and severity of WUI wildfire episodes and the necessity to improve community preparedness, planning strategies have to be devised that integrate foresight into wildfires, with active community participation. This paper presents an intelligent collaborative environment that seeks to engage citizens, planners, and emergency services in co-creation of fire-resilient strategies through Agentic AI-driven wildfire simulations and mixed-reality visualization. A serious game environment is designed for hands-on exploration of alternative wildfire spread scenarios and community-scale prevention practices such as prescribed burning, fuel treatment through vegetation control, and structural hardening measures. The objective is to promote public awareness and adaptive behavior as well as provide science-based operational decision support to emergency responders in evaluating tactical options specific to terrain, infrastructure, and fuel conditions of the locale. The system operates on a Large Language Model (LLM)–powered agentic AI architecture designed to automate and orchestrate 2D and 3D wildfire simulations, providing guidance that supports users from diverse technical backgrounds. To give the results of the simulations, 3D web visualization and immersive holographic display were used to enable cycles of iterated explorations into fire spread in dense urban settings. With AI-assisted wildfire intelligence, this particular flow works through a set of intuitive interaction mechanics so that communities can evaluate risk levels, weigh alternatives for mitigation, and better prepare for an actual fire event.

Accurate spatial alignment is a key requirement for situated Augmented Reality (AR) in urban planning, where citizens and planners can visualize proposed designs in real outdoor environments. However, existing AR localization approaches often rely on smartphone GNSS, vendor-specific cloud anchors, or cloud-based visual positioning, which introduce accuracy limitations, privacy concerns, or dependencies that restrict their use in participatory planning workflows. This paper presents a privacy-aware on-device localization pipeline for outdoor urban planning scenarios. The approach aligns LiDAR scans captured on smartphones with pre-scanned reference point cloud tiles to enable stable and accurate placement of urban planning models. Approximate GNSS is used only to retrieve a relevant reference tile, while all preprocessing and registration steps are performed locally on the device. The pipeline combines voxel downsampling, local geometric descriptors, and global registration to estimate alignment without relying on GNSS for pose estimation or on cloud-based visual localization services. A mobile demonstrator was developed to support situated AR in urban planning scenarios, allowing users to explore design proposals directly in context. Initial validation under controlled conditions showed that the system can recover translations and rotations with errors on the order of a few centimeters, while processing times remained suitable for mobile use. The approach was also deployed in an urban planning case study and enabled stable outdoor visualization of planning elements on-site.

We present a new method for assessing visual walkability using 360° videos and an eye-tracking in Cinematic Virtual Reality (CVR). Visual walkability refers to the walkability perceived by pedestrians through visual stimuli in the urban environment. Our method uses semantic segmentation, viewport exposure, gaze measures, and a custom walkability questionnaire, enabling comparison between scene content, participant's viewport, and their gaze focus. The 10 videos used, including 2 calibration videos, exhibit distinct semantic characteristics, validated by segmentation analysis. Analysis of the 35 participants’ responses shows that walkability ratings at the video level correlate with several environmental parameters (e.g., road, sidewalk, sky) consistent with previous studies. However, these parameters do not have a similar influence in gaze-based visual attention analysis within the CVR setting, suggesting that CVR attention would requiere further work. Furthermore, our results suggest that unexpected semantic classes may also play a role in perceived walkability and should be considered exploratory pending further validation. This paves the way for further research on using CVR as an assessment tool for visual walkability and for developing methodological guidance on which visual cues are robust across measures (content/viewport).

Oceanographic data and their related simulation have a key role in addressing EU and UN societal challenges in marine environments. Visualising marine data is challenging for different visual-communication intents and audiences, despite existing guidelines on the subject. A main visual-design limitation for existing techniques is the co-visualization of multiple hydrodynamic field attributes in an accessible, comprehensible and engaging manner. This paper addresses this limitation in two ways: first, existing techniques for cartographic-oriented design of waterlines are adopted and extended towards multivariate hydrodynamic field datasets. Secondly, experimental results on the intermixing different visual-channel mapping of hydrodynamic attribute data are presented in a case study on ocean-flow patterns around the Hebrides island chain (UK). The results demonstrate a simultaneous co-visualization of up to five unique, independent scalar attributes in a comprehensible manner while preserving the geographic context. Moreover, best-practice guidelines are stated in conclusion of the experimental case study to help oceanographic practitioners adopt the presented technology in their professional workflows.

Artificial light at night (ALAN) degrades nocturnal ecosystems and complicates astronomical observation. Although all-sky imaging and GIS-based light-pollution mapping are well established in the analysis of light pollution, identifying local contributors to ALAN still requires time-consuming cross-comparisons, done in separate views, making light halo--source attribution slow and manual. We present an interactive system that addresses this gap by co-registering Sky Quality Camera all-sky imagery and OSM-derived candidate emitters (e.g., settlements, roads, aerodromes, industrial sites) in one observer-centered scene. The viewer is placed at the locations of the captured all-sky images in 3D digital terrain model-based scenes, realistically illuminated by the sky under selected conditions for an immersive view of nighttime scenarios. OpenStreetMap features are projected onto a surrounding sphere via inverse stereographic projection, with point markers and horizontal-extent indicators to support rapid visual matching between observed halos and plausible sources. Users can switch scenes and processed sky images, adjust projection parameters, and inspect scenes in VR or in an additional cylindrical projection for a panoramic desktop view. A companion web tool configures location classes and display ranges. The presented system primarily targets exploratory analysis, with its main contribution being the novel co-visualization of light sources and light halos; expert interviews positively validated this analytical focus. As a secondary outcome, the system's immersive first-person representation may also enrich educational communication and outreach on ALAN impacts.

Accurate and intuitive visualization of urban development projects is a persistent challenge in spatial planning and public participation. Recent advances in Extended Reality (XR) offer new opportunities to integrate geospatial data directly within the user’s real environment. This paper introduces GeoWebXR, an extension of the WebXR API designed to provide absolute georeferencing of the XR reference space via a standardized geopose. We present an outdoor proof-of-concept implementation that integrates a dual-antenna RTK GNSS receiver mounted on an XR headset. High-precision GNSS measurements are fused with the device’s local pose estimates to compute a consistent and accurate geopose, enabling decimeter-level alignment between virtual and physical environments.

Leveraging GeoWebXR, WebGL applications can render georeferenced 3D content in situ through a web browser. We demonstrate this capability using the iTowns geospatial visualization framework to deliver an XR experience for urban planning. The system supports both 1:1-scale in-situ visualization and reduced-scale overview modes, enabling seamless multiscale exploration of planning scenarios. To mitigate cognitive overload in dense urban contexts, we implement and evaluate several visualization and interaction strategies. We assess the usability and spatial appropriation enabled by our system, and discuss how it may support both expert analysis and citizen participation in urban planning processes.

Hyperspectral images (HSIs) are often severely degraded by mixed noise, such as Gaussian, stripe, and impulse noise during acquisition and transmission, which seriously impedes their subsequent applications. Therefore, HSI denoising is both crucial and challenging. In this work, we present a gradient-domain outlier-guided texture-preserved total variation (OG-TPTV) regularizer designed to remove mixed noise in HSIs. First, we utilize the mode-3 low-rank property of HSI gradient maps along the spectral dimension and apply a low-rank decomposition model to extract their spatial representation coefficients (SRCs). To improve the sparsity characterization of SRCs in the gradient subspace, an outlier-guided strategy is introduced. Specifically, we perform outlier detection on gradient maps to distinguish noise from texture structures and remove outliers to generate precise texture weighting maps. The resulting texture weight maps offer adaptive guidance for adjusting the strength of the sparsity constraints. Finally, a denoising method for HSIs is developed based on OG-TPTV. Extensive experiments on both synthetic and real HSIs demonstrate the superior denoising performance of our method.

Hyperspectral satellite missions generate massive data volumes that are difficult to transmit and store under tight onboard resource constraints, making effective lossy compression a key enabling technology. We propose SpectralNet-X, a transformer-based autoencoder for spectral-only compression of spaceborne hyperspectral imagery at a fixed compression ratio of 16. The encoder maps each spectrum to a low-dimensional latent code using a 1D convolutional projection followed by stacked self-attention layers with rotary position embeddings, and aggregates information via cross-attention pooling. The decoder reconstructs full-band spectra through an upsampling stack and per-band affine calibration. To improve reconstruction fidelity and generalization, SpectralNet-X is first pretrained in a masked-signal reconstruction task inspired by SimMIM and then fine-tuned with a mixed objective that combines mean-squared error and spectral angle mapper (SAM) terms using a scheduled weighting scheme. We evaluate SpectralNet-X on the large-scale HySpecNet–11k benchmark and in a mission-realistic cross-sensor setting, where models trained on HySpecNet–11k are tested on PRISMA hyperspectral scenes. Across PSNR, SSIM, and SAM, and when compared to three different compression autoencoders, SpectralNet-X achieves the lowest angular reconstruction errors while maintaining competitive distortion metrics and substantially reducing the fraction of spectra with large SAM outliers. These results indicate that transformer-based spectral compression is a promising candidate for robust, mission-realistic onboard hyperspectral data reduction.

Validating the performance of deep learning models in satellite imagery is essential for ensuring model generalizability, decision reliability, and spatial transferability—particularly in the context of hyperspectral images, which contain high-dimensional, spatially complex data. While it is well recognized that multiple spatial characteristics influence deep learning model performance, few studies have systematically examined how the interactions among these characteristics affect model validation sensitivity in hyperspectral contexts. This study aims to investigate how the interaction between spatial scale (e.g., surrounding 3, 5, 7 grids) and training sample size (e.g., 10%, 30%, 50% of all data) influences the validation accuracy and sensitivity of deep learning models. An innovative validation sensitivity index is developed to quantify the change in accuracy per unit of spatial scale and sample size, enabling a more refined assessment of model robustness. The index is applied to three representative hyperspectral datasets, covering diverse environmental and spectral conditions. Results show that spatial scale accounts for 0~21.0% accuracy variation, training sample size contributes 5.6~36.5% variation, but their interaction leads to 5.4~70.3% variation, indicating a nonlinear amplification enhanced effect. These findings may be explained by the compounded influence of data contextuality, spatial redundancy, and model overfitting dynamics. This study demonstrates the critical need to consider spatial interactions in validation design, offering new insights for enhancing the reliability of geospatial artificial intelligence (GeoAI) applications in remote sensing and spatial data science.

Surface reflectance is a foundational variable in optical remote sensing, as inaccuracies introduced during atmospheric correction can propagate and amplify across subsequent satellite-derived products. Nonetheless, the extent to which the choice of Radiative Transfer Model (RTM) affects reflectance retrieval has not been sufficiently examined. This study investigates how two widely used RTMs—6SV and VLIDORT—produce different surface reflectance outcomes when applied under consistent atmospheric and geometric conditions for the GEO-KOMPSAT-2B/GEMS instrument. To ensure comparability, both models were driven by identical GEMS aerosol properties and an equivalent LUT configuration.

The comparison shows that while the two RTMs reproduce broadly similar spatial patterns, systematic quantitative differences remain in the retrieved reflectance. These differences vary depending on atmospheric and viewing conditions, particularly under higher aerosol loading. A sensitivity analysis further indicates that aerosol amount and scattering characteristics, alongside viewing geometry, are key factors influencing the magnitude of RTM divergence.

Overall, this study provides a structured assessment of RTM-dependent variability in atmospheric correction and highlights the importance of model choice when interpreting or harmonizing surface reflectance products. The findings offer a basis for improving consistency in future GEMS-based retrievals and for advancing reliable surface reflectance generation in geostationary remote sensing.

Remote sensing acquisition systems rely on a range of platforms, from drones to satellite missions, to record multimodal Earth surface data. This fact encourages the preparation of datasets with complementary properties, thereby increasing their discriminative potential. A common complementary combination is between Hyperspectral and LiDAR-generated digital surface model data. While engaging, this fusion poses challenges for specific applications. Multiple works fuse these modalities at the feature level using vector concatenation, maximization, or averaging. Although functional, these methods omit target interactions between the modalities. Another challenge in remote sensing is the quantity and quality of labels required by deep learning methods, which are expensive, error-prone, and difficult to scale. We address the challenges above by proposing a self-supervised processing framework based on cross-modal attention that effectively fuses features at multiple levels, thereby exploiting complementary information across data streams. Specifically, our method is founded on a pseudo-Siamese network that reweights each modality’s features with information from the other via a mirrored cross-modal attention. The network’s objective is to maximize the similarity between the feature representations of both streams. A fusion network builds a latent representation using the learned encoders and attention modules. Then, a k-Nearest Neighbor classifier categorizes each sample within the representation using ten labels per class. Our experiments show that our spatial- and channel-spatial cross-modal attention approaches outperform well-established fusion methods for label-efficient land cover classification across datasets. Our findings lay the groundwork for fusion methods that effectively exploit inter-stream data relationships to encourage complementarity.

Despite the rapid development of satellite sensors, acquiring high-resolution RGB images remains a challenge. In this paper, a GAN-based multiscale feature-based pan-to-rgb model is proposed to establish a novel framework for high-resolution, high-fidelity RGB images generation from remote sensing panchromatic images. The spatial structure, texture, and color of the results are consistent with the real images, and the colors are naturally realistic and vibrant. Multiscale features and symmetric luminance color decoders are utilized to overcome color desaturation, inaccuracy, and distortion in conventional algorithms. By combining CNNs for local feature modeling and transformers for global feature modeling, this approach learns pan-to-rgb mappings to produce high-resolution, high-fidelity RGB images in CIELAB space. Besides, the luminance distance loss and the color distance loss are utilized to prevent the coupling of luminance and color. We also conducted experimental validation on Gaofen-7 satellite data, and the results demonstrated that the FID, CF, and △CF indicators of the proposed algorithm improved by 2.90%, 11.77%, and 64.51%, respectively, compared to the comparison algorithms.

Deploying object detection models for resource-constrained remote sensing applications necessitates on-board model inference capabilities. While Field Programmable Gate Arrays (FPGAs) offer massive parallelism as energy-efficient hardware platforms, model quantization remains essential to further balance computational efficiency with detection accuracy. Compared to post-training quantization methods that involve multiple-stage development with consistent dependency on domain datasets, quantization-aware training (QAT) integrates quantization constraints into training, providing a simpler pipeline for model compression. However, QAT introduces quantization errors to which smaller objects are more vulnerable. To address this issue, we propose object-scale-aware (OSA) regularization that amplifies quantization error penalties for smaller targets. Our approach is validated through two case studies: bird detection at airports and aerial-view building detection. We perform 8-bit QAT on YOLOX series models using the MVA2023 dataset and the Bavarian Building Dataset for the respective studies. Our method achieves up to 50.2 times inference acceleration with minimal accuracy loss on Xilinx Kria KV260 FPGAs compared to full-precision models. The ablation study and detection examples further demonstrate the effectiveness of OSA regularization in small object detection.

A broad evaluation of state-of-the-art Visual Place Recognition methods is presented. The evaluation focuses on tasks where a fast image pair retrieval is of high importance, such as image-driven scene registration, SLAM or Structure-from-Motion correspondence search. This implies, that the focus of the study is geared away from typical Visual Place Recognition and towards scenarios of interest in computer vision and robotics. A sophisticated evaluation pipeline for retrieval and runtime performance is presented. Prepared datasets based on widely used benchmarks from different domains are utilized, such as indoor-SLAM, outdoor object-centric as well as autonomous navigation in urban and sub-urban areas.

3D object reconstruction, camera pose estimation, and novel view synthesis in industrial applications are challenging tasks, as errors are costly while the timewindow for solving these tasks is often limited. The complexity of typical industrial objects further complicates these tasks. Different datasets that can be used to evaluate current methods on these tasks exist, however, most of them do not depict realistic industrial scenarios. We introduce the Machine Vision Metrology Industrial Object Dataset (MVM-IOD)

that addresses this lack of datasets. The hardware setup to acquire the dataset consists of a camera, mounted upside down due to space restrictions, at the end effector of an industrial robot arm. Images of typical industrial objects are captured systematically, by moving the camera on a hemisphere around the objects. MVM-IOD contains the camera poses, the acquired RGB images, and the 3D point cloud of 9 objects and 2 background choices resulting in 18 scenes, which allows evaluation of all image based

methods that compute a 3D reconstruction, camera poses, and/or novel views. Based on our dataset, we extensively evaluate current state-of-the-art 3D reconstruction and camera pose estimation methods, such as Structure from Motion, Multi-View Stereo, Visual Geometry Grounded Transformer (VGGT), π3, as well as 2D Gaussian Splatting and report our findings to create a baseline for future research.

Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and

cross-domain generalization. Yet their lack of interpretability, tendency to be overconfident, and sensitivity to real-world domain

shifts pose critical challenges for safety- and mission-critical applications. Uncertainty quantification (UQ) offers a principled way

to address these issues, but its integration into segmentation foundation models has yet to be explored. In this paper we present the

first systematic evaluation of UQ methods applied to a foundation model for semantic segmentation. We fine-tune a lightweight

DPT decoder on top of the pretrained SAM2 encoder to establish a simple yet competitive baseline and benchmark four representative

UQ approaches – Monte Carlo Dropout, Deep Sub-Ensemble, Test-Time Augmentation, and Evidential Deep Learning – across

Cityscapes, NYUv2, and two challenging out-of-domain settings. Our analysis compares segmentation accuracy, calibration, uncertainty

quality, and inference time, revealing clear trade-offs between predictive performance, reliability, and computational cost.

These results highlight both the promise and the current limitations of uncertainty-aware foundation models, pointing to the need

for future work that jointly optimizes accuracy, robustness, and efficiency for real-world deployment.

Ensuring not only high accuracy but also reliable and robust predictions is critical for the deployment of semantic segmentation

models in safety-critical applications such as autonomous driving. Despite the widespread use of CutMix – a simple yet powerful

data augmentation strategy – its effect on the reliability and robustness in dense predictions tasks remains unexplored. Motivated

by recent findings that semi-supervised segmentation methods, where CutMix is a core component, can severely degrade reliability,

this study isolates and systematically analyzes the influence of CutMix on segmentation accuracy, calibration, and uncertainty

quality. We evaluate two representative architectures, the CNN-based DeepLabV3+ and the transformer-based SegFormer, across

both in-domain and out-of-domain scenarios. Our results show that CutMix has only a minor impact on segmentation accuracy

but consistently improves the reliability, particularly under distribution shifts. These improvements indicate that CutMix primarily

enhances the trustworthiness of the model’s calibration and uncertainty rather than the raw segmentation prediction itself. This

distinction is crucial for safety-critical deployment, where reliable confidence estimates are as important as raw performance.

Visual Geometry Grounded Transformer (VGGT) has already attracted a great deal of attention in a short period of time, not least due to the Best Paper Award at CVPR-2025. Similar to DUSt3R and MASt3R, VGGT aims to bring about a paradigm shift by replacing established methods like bundle adjustment and feature matching with a simple, unified, feed-forward neural network that predicts camera poses, depth maps, and dense 3D structure directly from multiple images of a scene in a few seconds. A key aspect is its ability to process an arbitrary number of views consistently in a single forward pass without any post-processing or iterative optimization. For photogrammetry, this opens new possibilities for real-time, scalable, and accessible 3D reconstruction. In this context, not only high reconstruction accuracy but also high-quality uncertainty estimates are crucial, as they foster trust and enable robust quality assurance. This paper therefore investigates the quality of VGGT’s uncertainty predictions. The analysis identifies an effective confidence threshold for filtering VGGT’s raw output and demonstrates that enhancing uncertainty quality holds strong potential for improving the accuracy of its 3D reconstructions.

WebGIS platforms have revolutionized geospatial data dissemination, yet their adoption remains constrained by the steep learning curve of mapping library APIs. Frontend libraries like Leaflet, OpenLayers, and platforms such as Tianditu contain hundreds of classes and methods, requiring substantial programming expertise. This technical barrier prevents domain experts—urban planners, environmental scientists, public health officials—from independently creating the visualizations they need for analysis and decision-making.While Large Language Models (LLMs) have revolutionized code generation, they struggle with domain-specific, low-resource APIs common in geospatial applications. When applied to specialized geospatial APIs, these models exhibit critical failures: they frequently "hallucinate" non-existent functions, misuse parameters, or generate syntactically plausible but semantically incorrect code. This unreliability stems from the underrepresentation of domain-specific libraries in LLMs' training corpora, creating a "last mile" problem that renders them unsuitable for professional geospatial development. This study proposes a ReAct-based agent framework for automated web map generation from natural language instructions. The framework constructs a stateful, cyclic workflow and enables human–AI interactive WebGIS code generation based on the Tianditu JavaScript API. Its effectiveness and generality are validated through multi-model evaluation (GPT-4, Claude 3, Llama 3, Qwen-Max), demonstrating robust performance across diverse application scenarios. Experimental results show that the framework achieves professional-grade quality in both directive-driven and data-driven geospatial visualization tasks.

With the proliferation of online geospatial services, Geospatial Application Programming Interfaces (GeoAPIs) have become the backbone of modern spatial data interoperability. However, the high technical barriers of GeoAPIs, characterized by complex RESTful syntax and deterministic parameter requirements, create a significant "digital divide" for non-expert users. To bridge the gap between intuitive human spatial intent and technical service execution, this study proposes Text2GeoAPI, a novel conceptual framework for the automatic invocation and composition of geospatial services via natural language. We introduce the Intent-Entity-Operation (IEO) model to formalize spatial tasks, decoupling high-level semantic goals from atomic technical operations. We developed a modular prototype leveraging Large Language Models (LLMs) as cognitive engines to perform structured intent parsing, dynamic workflow planning, and multi-source result synthesis. Experimental evaluations using 100 diverse spatial queries demonstrate an overall task success rate of 86%, with the system effectively orchestrating multi-hop service chains (e.g., Geocoding → Isochrone Analysis → POI Search). The results confirm that Text2GeoAPI significantly lowers the threshold for accessing professional geospatial analysis, shifting the GIS paradigm from "tool-centric" to "intent-centric" intelligence.

Winter accessibility poses critical challenges in cold-climate cities such as Québec, where snow and ice accumulation restrict the mobility of people with disabilities. This study presents an AI-driven multimodal framework designed to detect, classify, and

map winter barriers affecting pedestrian accessibility in Québec City. Building upon the SNOWMAN project, synthetic image and textual datasets were developed to represent seven major snow- and ice-related obstacle categories, including icy ruts, deep

snow, and uncleared sidewalks. The visual modality employed a self-supervised SimCLR model for snow-barrier classification (F1-score = 0.93), while the textual modality used a fine-tuned BERT classifier, achieving a perfect F1-score = 1.00 on validated

synthetic descriptions. Canonical Correlation Analysis (CCA) aligned the two modalities into a shared latent space, enabling spatial fusion of visual and semantic embeddings for integrated analysis within the MobiliSIG Winter Mobility platform. The fused data

produced dynamic accessibility maps revealing clusters of recurring winter hazards in known high-risk zones. The results confirm the feasibility of using synthetic multimodal data to simulate pedestrian-scale winter conditions and demonstrate the potential of

multimodal AI for inclusive, data-driven mobility management in cold-climate cities.

As China’s urban development shifts toward stock-based optimisation, identifying inefficient residential land has become important for urban regeneration. Existing approaches often rely on subjective weighting, linear analytical structures, or homogeneous treatment of different residential types, which weakens robustness and transferability. To address these limitations, this study proposes a data-driven framework that integrates mobile-phone signaling and other multi-source spatiotemporal big data in Shenzhen. Two dominant residential forms—formal residential communities and urban villages—are evaluated separately through a four-dimensional framework covering built form, activity vitality, economic efficiency, and environmental livability. Principal component analysis is used to estimate intrinsic dimensionality and initialize a parametric autoencoder. A spatially constrained Gaussian mixture model is then employed to identify inefficient residential clusters while preserving local coherence. The clustering results are interpreted using a random forest model and TreeSHAP, and externally validated by street-view imagery interpretation and limited field surveys. PCA retained five components for urban villages and six for formal residential communities, and the BIC selected six and five clusters for the two residential types, respectively. The results indicate that inefficient formal residential communities show scattered and island-like spatial patterns, whereas inefficient urban villages tend to form more continuous clusters along the edges of larger village agglomerations. Random forest and TreeSHAP further reveal that inefficient urban villages are more strongly associated with deficiencies in service accessibility and local socioeconomic conditions, whereas inefficient formal residential communities are more closely associated with lower residential vitality and relatively high development intensity. External validation indicates acceptable agreement with observed residential conditions.

This paper investigates the long-term consistency and reliability of paid geospatial crowdsourcing on the online platform Microworkers.com. Over a five-month period, we conducted three crowdsourcing campaigns, each representing a task typical for remote sensing, i.e., pixel classification, point selection, and geometric outline acquisition, to assess whether repeated worker participation enhances data quality and reproducibility. Beyond individual task performance, we examine the broader question of whether crowdsourcing campaigns can yield reproducible results over extended periods. Despite the large and heterogeneous workforce of Microworkers.com, a substantial share of tasks was completed by recurring workers who consistently outperformed one-time participants. Furthermore, across all campaigns, data quality remained largely stable, with only minor variability between epochs. Additionally performed statistical analyses confirm that reproducible outcomes are achievable, highlighting the potential of reliable and reproducible crowdsourcing results for geospatial data acquisition.

This abstract presents a comprehensive research framework examining the interplay between colonial trade networks and the spatial form of port cities during the early modern era. Firstly, the study constructs a geographic database of nearly 300 colonial port cities, using intercity trade data from East India Company archives as network edges to analyze their structural and morphological evolution. Secondly, it processes historical maps of colonial ports through a fine-tuned multimodal large language model to extract and classify spatial morphological features, establishing a systematic typology of urban form patterns. Thirdly, the research develops regression models to reveal correlations between network status and morphological patterns. Preliminary findings highlight Batavia's dominant yet volatile role within the network and reveal a trend toward decentralization over the 18th century. The research contributes to both urban historical studies and digital humanities by offering a scalable, comparative approach to interpreting colonial port cities as spatial manifestations of global economic and political forces, while establishing empirical relationships between network status and urban form characteristics. It further provides a refined framework for contextualizing their cultural heritage significance within trans-colonial networks.

This study explores the application of Graph Convolutional Networks (GCNs), specifically the GraphSAGE model, to the cartographic generalization of hydrographic networks in the state of Santa Catarina, Brazil. The generalization of river segments is critical for transitioning from detailed (1:25,000) to generalized (1:100,000) scales. It's traditionally a manual, rule-based process. By modeling drainage systems as graphs and training deep learning models with data from the Brazilian Army's Geospatial Database (BDGEx), this research evaluates how geometric and semantic attributes influence generalization outcomes. This data follows Brazilian Technical Specifications of the Geospatial Vector Data Structure (ET-EDGV), therefore they figure as a systematic data from Brazilian institutions.

GraphSAGE model was trained four times, each incorporating varying combinations of attributes such as segment length, sinuosity, polygon containment, and river flow regime. The model trained with all attributes achieved the highest accuracy (99.98%). Even models using geometric features surpassed 93% accuracy. These results highlight the effectiveness of GCNs in capturing structural patterns.

This study compares GraphSAGE model outputs to those generated by the GeoData Loader for Mapserver (GDLMS), the current operational system for generalization, developed and used by the Geographic Service of the Brazilian Army. It also compares those generalization to reference data acquired by manual generalization using the same 1:25.000 scale input. Visual analysis in GIS environments reveals that GCNs can be an alternative for generalization tasks.

This research demonstrates the viability of using GeoAI methods for automating complex cartographic processes, offering a scalable and data-driven solution aligned with national geospatial data standards.

This study statistically analyzes fire events in the Atlantic Forest, seeking to understand their particularities in relation to the national scenario. The biome, historically pressured by deforestation, fragmentation, and anthropogenic activities, also suffers from agricultural, livestock, and accidental fires, which increase its vulnerability. The research used data from Censipam's Fire Panel, obtained by MODIS and VIIRS orbital sensors, considering records from 2020 onwards and specific sections for the Atlantic Forest. Variables such as area, severity, persistence, speed of expansion, number of outbreaks, Fire Radiative Power (FRP), and detections were analyzed. The results indicate that, compared to the national pattern, fires in the Atlantic Forest are less intense and shorter in duration, a phenomenon associated with higher humidity, landscape fragmentation, and management conditions. It is concluded that the dynamics of fire in the biome differ significantly from the national average, reinforcing the importance of regional monitoring and firefighting strategies aimed at preserving its ecological integrity.

The MODIS burned area product is widely used to extract ignition locations and delineate individual fires for wildfire probabilistic loss modeling. However, limited studies have systematically evaluated the accuracy of these derived fire events through detailed spatial and temporal comparisons with reference datasets. This study addresses this gap by developing a robust framework to assess the accuracy of MODIS-derived individual fires across the United States. In this study, the MODIS Collection 6 MCD64 burned area product was used to extract ignition locations and individual fire events using the Fire Events Delineation (FIRED) algorithm. A comprehensive evaluation framework was then implemented to assess the delineated fire events against the Monitoring Trends in Burn Severity (MTBS) reference dataset, accounting for both spatial overlap and temporal consistency. The results show that the proposed approach achieved an average Intersection over Union (IoU) score of 0.54, an F-score of 0.701, an overall accuracy of 0.77, a precision of 0.90, and a recall of 0.57. These metrics represent averages across the period 2001–2020. Collectively, the results highlight the strengths and limitations of the event detection system and provide a quantitative assessment of its performance. This comprehensive evaluation offers valuable insights into the reliability of MODIS-derived individual fire events and improves understanding of their suitability for wildfire probabilistic loss modeling and related applications.

Wildfires cause economic, social, and environmental consequences, as they affect ecosystems, public safety, biodiversity and natural resources. They pose challenges to various world regions, particularly Mediterranean areas such as Spain. Numerous fire prediction and detection systems were introduced to detect and predict fires as well as prevent their risks and damage. Statistical methods and classical machine learning models were often employed to estimate and predict fire risk, showing their efficiency in generating fire risk maps. However, they fail to accurately capture complex temporal and spatial characteristics related to fire ignition. To address this challenge, a novel Convolutional Neural Network (CNN) model, namely CFMap, was introduced for predicting and generating detailed wildfire risk maps covering Spain regions. Comprehensive analyses were performed using data between 2008 and 2024, including fire history, geographical location information, land usage features, human activity indices, topography data, meteorological features, and vegetation indices from Spain regions, collected from the IberFire dataset. CFMap showed a superior performance with an accuracy of 0.8028 ± 0.0440, an AUC (Area Under the Curve) of 0.9354 ± 0.0088, and an F1-score of 0.7787 ± 0.0623, outperforming classical machine learning methods (XGBoost, LightGBM, and RandomForest) and deep learning models including ResNet and a simple CNN. These results demonstrate its reliability in predicting fire events and generating monthly fire risk maps for different Spain regions. Consequently, it helps to identify high fire risk zones, improve fire management strategies, and efficiently deploy firefighting resources, thereby reducing the potential risk and impact of fires.

In the immediate aftermath of a hurricane, the rapid, accurate assessment of building damage is paramount for effective emergency response and the allocation of resources. Traditional methods of damage assessment, which rely on ground-based surveys, are often slow, hazardous, and subjective. While the advent of remote sensing (RS), through Unmanned Aerial Vehicles (UAVs) and the application of Convolutional Neural Networks (CNNs), has significantly advanced the automation of this process, these models operate on a pixel-level or object-level basis, failing to capture the inherent spatial relationships and contextual information within a disaster zone. Damage patterns are not spatially random; they exhibit strong spatial autocorrelation, a principle encapsulated by Tobler's First Law of Geography. This paper introduces a novel approach that leverages Graph Attention Networks (GATs) to explicitly model spatial dependencies when evaluating building damage. By representing damaged buildings and their surroundings as nodes and edges in a graph, our model can learn and weigh the influence of neighboring structures and the local environment when assessing their damage level. This spatially-aware methodology moves beyond simple image classification to a more holistic scene understanding. We evaluate the method on DoriaNET, a geo-referenced UAV dataset collected after Hurricane Dorian (2019) that provides masked building patches, GPS centroids, structural metadata, and ordinal FEMA/HAZUS-style damage labels. By incorporating spatial context via a graph-based framework, our GAT model achieves superior performance in building damage classification compared to state-of-the-art CNN-based approaches, producing more coherent and accurate damage maps better suited to real-world disaster management scenarios.

This work presents an innovative image-based method for measuring wind speed and direction in urban environment using video

footage. Wind dynamics are traditionally investigated at multiple spatial scales, including pollutant dispersion at the canopy level

(Allwine, 2000), architectural design and outdoor comfort at the building scale (Allard, 2012; Holst, 2011) and the convection heat

transfer coefficient ℎ [Wm-²K-1] used to define the boundary conditions of numerical simulations (Oke, 2017). In 1997, Gary Settles

showed that image measurement could provide non-invasive and high-resolution measurements of fluid motion. This paper presents a

method for extracting anemometric data from images at the urban scale. We process freely accessible videos from the internet in which

air masses are identified at the canopy level. Motion extraction technique is used to isolate elements of the video that are in motion.

This information is fed into an optical flow algorithm that estimates an apparent velocity in [pixels/frame]. To convert the data to

[km/h], the view’s perspective is considered to ensure the conversion is accurate across the entire image. Distance mapping is

performed by projecting the image onto a 3D model of the scene, and the camera's recording parameters are estimated by simulating

the illumination of the scene. The anemometric data obtained are evaluated in relation to meteorological data recorded at a nearby

weather station. Innovative and simple to implement, this approach provides estimates of wind speeds and directions that are both

reliable and directly usable for architectural design and climate studies.

Rapid urbanization and the loss of vegetative cover in Indian cities have raised serious concerns about environmental sustainability and public health. This study focuses on analyzing and forecasting Urban Heat Island (UHI) patterns in Jaipur, India, by examining both Surface UHI (SUHI) and Atmospheric UHI (AUHI). Using Google Earth Engine, the research integrates diverse spatio-temporal datasets—including Landsat-derived indices (such as LULC, NDVI, NDWI, NDBI, NDMI, albedo, and emissivity), geospatial features (building density, sky view factor, and population density), and meteorological data (air temperature, humidity, wind speed, and solar radiation) from 2000 to 2024—to train a Random Forest Regression model. The model demonstrated strong performance (R² = 0.806; RMSE = 0.059), surpassing linear and generalized additive models by effectively capturing complex, non-linear relationships. It also helped identify high-risk areas like Transport Nagar and Budhsinghpura. Projections for 2030 and 2035 indicate increasing heat stress, particularly in Jaipur’s expanding urban periphery. This GIS-integrated machine learning framework presents a replicable approach for UHI prediction in other fast-growing Indian cities.

Persistent Scatterer Interferometry (PSI) is a consolidated active remote sensing technique to measure and monitor land deformation. The technique has experienced an intense development in the last 25 years. PSI techniques use large stacks of SAR images that cover a given observation period. The outcome of any PSI processing is a cloud of geocoded measurement points that contain the estimated deformation time series over the observation period. If the analysed area is wide, the corresponding point cloud can be huge. In these cases, the potential users often experience problem in analysing such huge point clouds, and this can limit the PSI exploitation. In this paper we present a set of products that address specific application needs or that offer higher-level products with respect to the standard PSI products, which can facilitate the interpretation and exploitation of the PSI results.

Interferometric SAR (InSAR) is a well-established remote sensing technique to measure and monitor land deformation. We focus in this paper on Persistent Scatterer Interferometry (PSI) techniques based on large stacks of SAR images. Several PSI approached have been proposed in the last three decades, see for a review Crosetto et al. (2016). In this paper, we describe an approach the is based on the direct integration of the interferometric phases (back-to-back approach).

The Persistent Scatterer Interferometry (PSI) is a multi-temporal InSAR approach that allows to monitor displacement time series of the Earth's surface. The method identifies and analyzes Persistent Scatterers (PSs) which are phase stable scattering points which dominate the backscatter of their resolution cell. Standard PSI techniques only identify and analyze PSs which are coherent throughout the whole considered SAR time series. However, PSs can fade, appear or be occluded during the time series, forming so called Temporary PSs (TPSs), which should be integrated into the PSI to establish optimal measurement point networks. Previous research has proposed methods to integrate such TPSs into the PSI, however these were exclusively evaluated for construction-related TPSs. In this work, we evaluate the performance of a TPS integration method in handling recurringly occured PSs, and compare the performance of individual components of the algorithm against alternative methods. We evaluate the methods using simulated TPSs with temporal and spatial baseline settings taken from real Sentinel-1 data stacks. Furthermore, we present and discuss the application of the methods to a Sentinel-1 data stack acquired over the Oetztal Alps, which are seasonally covered by snow. We show that the integration of ROPSs significantly increases the measurement pixel density at many locations across the study area, compared to results from the European Ground Motion Service. Even if most of the ROPS did not have identified coherent segments in each covered summer with the current analysis algorithm, their integration leads to a significant information gain compared to standard PSI approaches.

Deformation monitoring in open‑pit mining and tailings‑dam operations is critical for operational safety, yet conventional in situ geodetic techniques provide only sparse, point‑based measurements. InSAR offers many displacement measurements, but its operational uptake is limited by complex workflows and the difficulty of interpreting analysis‑ready products such as EGMS. In cold regions, seasonal data gaps can introduce phase‑unwrapping artefacts that appear as winter‑only displacement offsets of approximately half the Sentinel‑1 wavelength. We propose a semi‑automated workflow to post‑process EGMS displacement time series, including pre‑filtering to identify and remove points affected by phase‑unwrapping errors and subsequent time‑series clustering in either a reduced‑dimensional representation or the full feature space. Cluster selection is automated using heuristic criteria and a custom metric based on temporal homogeneity and consistency. The findings show that the semi‑automatically detected clusters are plausible with regards to a visual interpretation of the EGMS data. The workflow supports improved interpretation of EGMS time series and avoids hard‑coded thresholds or reliance on velocity‑based estimates.

The Inglewood Oil Field, located in the Los Angeles Basin, California, is a major urban hydrocarbon production site with a documented history of ground deformation linked to oil extraction. To assess ongoing deformation and validate previous monitoring results, Interferometric Synthetic Aperture Radar (InSAR) analysis was conducted using Sentinel-1 SAR data processed through the Alaska Satellite Facility’s HyP3 platform and the Miami InSAR Time-series software in Python (MintPy). The study analysed ascending and descending datasets acquired between 2020 and 2025 to derive high-resolution deformation time series and velocity maps. Results reveal a localized deformation pattern characterized by low-magnitude vertical motion, with maximum uplift and subsidence rates of approximately +0.8 cm/yr and –1.6 cm/yr, respectively. Minor horizontal displacements (±1.0 cm/yr) suggest limited lateral strain associated with reservoir compaction and stress redistribution. Compared with previous assessments conducted up to 2024, the current findings indicate a marked reduction in deformation magnitude, implying progressive stabilization of reservoir pressure and improved subsurface management. These results demonstrate the effectiveness of InSAR for long-term monitoring of urban oilfields, providing critical insights into the behaviour and contributing to risk mitigation in densely populated environments.

Ground deformation caused by excessive groundwater extraction has become a major environmental concern in agricultural regions worldwide. Interferometric Synthetic Aperture Radar (InSAR) enables large-scale monitoring of ground deformation. However, its performance often decreases in low-coherence areas affected by vegetation growth and irrigation. In this study, we conducted a comparative evaluation of three multi-temporal SBAS-InSAR processing frameworks, MintPy, LiCSBAS, and SARvey, to assess their consistency in monitoring ground deformation across Golestan Province, Iran, using Sentinel-1 data acquired between 2014 and 2024. The analysis included deformation velocity fields, cross-sectional profiles, and time-series displacements, which were compared with temperature and precipitation variations. All three frameworks identified a pronounced deformation zone in the Gorgan Plain, with maximum line-of-sight deformation rates up to 13 cm/year. Quantitative comparisons showed strong correlations among the frameworks (r = 0.80 to 0.89), confirming their mutual reliability even under low coherence conditions. The time-series analysis revealed clear seasonal deformation patterns, with summer subsidence and winter uplift closely related to hydroclimatic fluctuations. Overall, this study demonstrates that multi-temporal SBAS-InSAR approaches can provide consistent and physically meaningful deformation estimates in challenging agricultural environments, offering valuable insights for subsidence monitoring and water resource management.

Urban rooftop solar planning relies on solar radiation inputs, yet estimates vary across models and measurement methods. This study compares radiation estimates from ArcGIS Solar Analyst, NASA solar radiation values, in-situ observations from research-grade and personal weather stations, and SDT-based Solar Radiation Modelling. We derive hourly global horizontal irradiance (GHI) values from these solar radiation data centres, model building-level estimates, harmonise all sources through temporal alignment, and then evaluate the values. The comparison reveals the hourly modelling of solar radiation models and common solar radiation centres, highlighting where an urban-adjusted local sensor provides lower solar radiation values because of the limited representation of the built and urban environment. Results show that utilising gridded or terrain-based models over urban-adjusted solar radiation values overrepresent due to the uncaptured localised shadings, roof placement effects, and increasing systemic errors for downstream rooftop PV terrain-based assessments. The cross-validated workflow of sensor-based city-scale solar radiation modelling is reproducible and scalable, offering local governments a more nuanced understanding of their solar capacity, and paves the way for carbon emission budget management.

Deep learning, in particular, has driven hundreds of new studies in remote sensing each year. However, ensuring the reliability of these models requires robust uncertainty quantification, an aspect that remains insufficiently explored. Current remote sensing deep learning models typically yield single, deterministic predictions, such as a class label for each pixel or a single biomass value for a given location or region. While commonly used metrics such as RMSE or classification accuracy summarize overall model performance, they fail to convey the reliability of individual predictions, leaving users without guidance on how much confidence to place in each output. Uncertainty estimation addresses this critical gap by quantifying the variability or confidence associated with model predictions. This enables practitioners to interpret not only what the model predicts but also how confident it is in those predictions, providing a more nuanced understanding that is essential for informed decision-making.

We address aleatoric uncertainty using Sentinel-1 and Sentinel-2 time series, proposing two approaches: (i) Gaussian UC, which predicts mean and standard deviation, and (ii) Quantile UC, which estimates the 10th, 50th, and 90th quantiles to capture asymmetric errors. We evaluate these approaches on three representative EO tasks: building height, canopy height, and aboveground biomass estimation. Our results (ID and OOD) show that both models achieve accuracy comparable to deterministic benchmarks while providing well-calibrated, interpretable, and operationally useful confidence estimates. Notably, both models outperform existing global canopy height products on evaluated sites, including the recent 1 m canopy height maps produced by vision transformers.

Recent advances in remote sensing have shifted the focus from the analysis of individual image scenes to the understanding of complex earth systems. This means that the analysis of dynamic evolutions replaces previous static examinations for fixed time points. Furthermore, interdisciplinary research and the integration of heterogeneous data sources are characterizing this transformation process. Digital Elevation Models (DEMs) are predestined for supporting this process by supplementing orthophotos and map data. Promising applications include city planning, landslide analysis, and flood risk assessment where spatio-temporal change detection is a central concept to be applied. Concerning map data, the OpenStreetMap project, based on the idea of Volunteered Geographic Information, has revolutionized the effective production and update of digital maps. However, OSM data does not include elevation information and often contains incorrect geometric information. In this paper, we introduce a self-training framework for validating OSM building footprints with the aid of high-resolution DEMs. The framework supports building segmentation with a self-supervised approach to improve the representation of OSM building footprints. The availability of Digital Elevation Models is used to check the quality of OSM data. The applicability of the approach is demonstrated by a case study conducted in Karlsruhe, Germany. The promising results are described in detail. With our approach, change detection of OSM data can also be carried out using different temporal versions of DEM and OSM data.

Accurate and reliable road extraction from LiDAR data remains a major challenge when spectral cues are limited or spatial heterogeneity increases model uncertainty. This study introduces a comparative, entropy-driven framework for evaluating the performance and reliability of road asset detection using three supervised machine learning algorithms—XGBoost, Random Forest (RF), and Support Vector Machine (SVM). Using a high-density aerial point cloud, a reproducible computational pipeline was implemented, to help practitioners in real-world scenarios for selecting the most robust and reliable machine learning methods for large-scale road assets mapping. Beyond traditional accuracy metrics (Overall Accuracy, F1-score, and Kappa coefficient), uncertainty-based evaluation of the outputs has been conducted using KPIs of entropy and sensitivity to training sets to quantify model reliability and spatial instability. Results reveal that the inclusion of RGB significantly reduces entropy across all models. XGBoost achieved the lowest mean entropy (0.084–0.143) and the most consistent probabilistic behaviour, reflecting confident and well-calibrated model. SVM, while statistically the most accurate (OA and Kappa > 0.97), exhibited higher local entropy (≈ 0.23–0.26), implying precise yet less certain classification. RF demonstrated the highest entropy (≈ 0.65–0.70) and the greatest variability, underscoring its sensitivity to feature noise. Under the WOR configuration, mean entropy rose markedly—most for RF_WOR (≈ 0.93) and moderately for SVM_WOR (≈ 0.39)—while XGBoost retained low uncertainty.

Spatial entropy maps further highlighted that uncertainty concentrates along road edges with RGB data but expands diffusely under WOR conditions, emphasizing the critical role of spectral–spatial synergy in constraining ambiguity. entropy-based evaluation provided insights beyond conventional accuracy metrics, revealing paradoxes between correctness and confidence.

Efficient processing of large-scale 3D point clouds acquired from Terrestrial or Airborne Laser Scanning (TLS/ALS), presents a significant computational challenge. While transformer-based architectures excel at modeling the global context crucial for interpreting these complex scenes, their quadratic computational complexity makes them infeasible for direct application on massive point sets. To address this scalability bottleneck, we propose the Spatio-Sequential Point Transformer (S2PT), a novel hierarchical architecture for efficient and effective large-scale point cloud processing. Our approach begins by serializing the point cloud into an ordered sequence, which enables the use of attention with linear complexity. This not only circumvents the quadratic bottleneck of standard transformers but also establishes a global receptive field at every layer. To compensate for potential information loss during serialization, we further introduce a novel Spatio-sequential Positional Encoding (S2PE) that synergistically combines 3D local geometric features with 1D sequential order information, enhancing the model’s spatial awareness. Experiments on multiple benchmarks demonstrate that S2PT achieves performance comparable to state-of-the-art methods while being significantly more efficient during training and inference, offering a promising path towards scalable representation learning for large-scale 3D scenes.

Accurate semantic segmentation of 3D point clouds is a fundamental task in photogrammetry, robotics, and large-scale scene understanding. Despite recent advances in point-based architectures such as PointNeXt, segmentation performance remains limited near semantic boundaries, where local neighborhoods often contain points from multiple classes, leading to feature ambiguity and oversmoothing.

In this paper, we propose a lightweight boundary-aware learning framework that explicitly models boundary regions during training. Boundary supervision is automatically derived from local semantic label disagreement, eliminating the need for additional annotations. An auxiliary boundary prediction head is introduced to learn boundary-sensitive features, which are subsequently integrated into the segmentation process through a late-stage feature fusion mechanism. In addition, a boundary-aware loss formulation emphasizes boundary regions during optimization, encouraging improved feature discrimination at class transitions.

Experimental results on the S3DIS dataset using the standard 6-fold cross-validation protocol demonstrate consistent improvements over the PointNeXt baseline. The proposed method achieves gains of 3.22% in mean Intersection over Union (mIoU) and 2.85% in mean class accuracy (mACC), with notably improved segmentation quality at object boundaries. Importantly, these improvements are obtained without modifying the backbone architecture or increasing inference complexity.

The results indicate that incorporating boundary-aware supervision provides an effective and efficient strategy for improving segmentation performance in challenging regions.

China is actively pursuing synergistic governance to address air pollution and carbon mitigation issues. This study, focusing on concentration as a key feature, assesses the synergy performance in the Yangtze River Delta Urban Agglomeration (YRDUA), revealing fluctuating trends with only seven cities showing improvement. To further understand the influences from local drivers, we employed an explainable spatial machine learning approach, integrating Geographical Weighted Regression (GWR), Random Forest (RF), and Shapley Additive Explanation (SHAP) to capture nonlinear, threshold, and interaction effects among explanatory variables. The analysis identifies longitude, SO2 emissions from industrial sources, wind speed, latitude, and the proportion of GDP from tertiary sector as the top five influencing factors, emphasizing the importance of geographical position, local air pollution emission, and meteorological condition. Most drivers exhibit nonlinear impacts and interactions with clear thresholds. Such as, wind speeds, exceeding 9.3 m/s negatively impact synergy. Furthermore, spatial heterogeneity of drivers' influence is evident across cities and regions. Specifically, cities along the eastern coast benefit from geographical advantages that enhance synergy in air quality improvement and carbon mitigation. Meteorological conditions, especially wind speed, significantly influence synergy, with notable differences between northern and southern coastal cities. These findings underscore the need for locally tailored governance strategies that leverage each city's unique geographical and socioeconomic attributes to enhance synergistic governance effectiveness. This research contributes to understanding the complex interplay of local drivers influencing synergistic governance in the YRDUA, providing valuable insights for policymakers aiming to improve air quality and promote sustainable development in rapidly urbanizing regions.

This paper addresses the heavy reliance on manual operations in control point acquisition for UAV photogrammetry and proposes an encoded control point deployment and recognition method based on a Light-Marker UAV (LMUAV). Conventional approaches rely on manual placement of control points and manual identification and measurement in images for aerial triangulation, resulting in low efficiency. To address this limitation, an LMUAV equipped with an LED array actively broadcasts its positional information as quaternary optical signals. The observing UAV performs coarse localization of the target region by integrating communication priors with the imaging model, followed by light spot segmentation and graph construction within the region of interest (ROI). Node correspondences are then recovered by constructing a template graph and an observation graph and applying Reweighted Random Walks (RRWM) graph matching. The matching robustness is further enhanced by incorporating directional point constraints and RANSAC-based geometric filtering. Based on the recovered correspondences, the encoded information is decoded through color recognition and validation, enabling automatic control point recovery. Experimental results in a cross-flight-line scenario with a single target UAV demonstrate that the proposed method achieves stable node matching and encoding–decoding, with a sequence-level accuracy of 76.32%, and a final effective decoding rate of 71.05%, while maintaining centimeter-level positioning accuracy, thereby validating its effectiveness for automatic control point acquisition in UAV mapping.

Automated and selective harvesting of fruits is increasingly vital due to high costs and seasonal labor shortages in advanced economies. This paper explores 6D pose estimation of strawberries using synthetic data generated through a procedural pipeline for photorealistic rendering. We utilize the YOLOX-6D-Pose algorithm, a single-shot method leveraging the YOLOX backbone, known for its balance of speed and accuracy and its suitability for edge inference. To counter the lack of training data, we develop a robust and flexible pipeline for generating synthetic strawberry data from various 3D models in Blender, focusing on enhancing realism compared to prior efforts, thus providing a valuable resource for training pose estimation algorithms.

Quantitative evaluations show that our models achieve comparable accuracy on both the NVIDIA RTX 3090 and Jetson Orin Nano across several ADD-S metrics, with the RTX 3090 offering superior processing speed. However, the Jetson Orin Nano is particularly effective for resource-constrained environments, making it ideal for deployment in agricultural robotics. Qualitative assessments further validate the model's performance, demonstrating accurate pose inference for ripe and partially ripe strawberries, although challenges remain in detecting unripe specimens. This highlights opportunities for future enhancements, particularly in improving detection for unripe strawberries by exploring color variations. Moreover, the presented methodology can be easily adapted for other fruits, such as apples, peaches, and plums, broadening its applicability in agricultural automation.

Photogrammetric 3D reconstruction has long relied on traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) methods, which provide high accuracy but face challenges in speed and scalability. Recently, learning-based MVS methods have emerged, aiming for faster and more efficient reconstruction. This work presents a comparative evaluation between a representative traditional MVS pipeline (COLMAP) and state-of-the-art learning-based approaches, including geometry-guided methods (MVSNet, PatchmatchNet, MVSAnywhere, MVSFormer++) and end-to-end frameworks (Stereo4D, FoundationStereo, DUSt3R, MASt3R, Fast3R, VGGT). Two experiments were conducted on different aerial scenarios. The first experiment used the MARS-LVIG dataset, where ground-truth 3D reconstruction was provided by LiDAR point clouds. The second experiment used a public scene from the Pix4D official website, with ground truth generated by Pix4Dmapper. We evaluated accuracy, coverage, and runtime across all methods. Experimental results show that although COLMAP can provide reliable and geometrically consistent reconstruction results, it requires more computation time. In cases where traditional methods fail in image registration, learning-based approaches exhibit stronger feature-matching capability and greater robustness. Geometry-guided methods usually require careful dataset preparation and often depend on camera pose or depth priors generated by COLMAP. End-to-end methods such as DUSt3R and VGGT achieve competitive accuracy and reasonable coverage while offering substantially faster reconstruction. However, they exhibit relatively large residuals in 3D reconstruction, particularly in challenging scenarios.

This study presents a comprehensive evaluation of six deep learning models for building exterior crack detection using UAV imagery. Our framework systematically compares Standard U-Net, ResNet34-UNet, UNet-Attention, UNet-Residual, HybridUNet, and TransUNet through rigorous ablation experiments. The models were trained on dedicated drone-captured crack imagery and evaluated using multiple loss functions and performance metrics.

Results show that TransUNet achieves optimal performance (87.66% F1 Score, 90.43% Precision, 89.99% Recall) by leveraging Transformer-based global context modeling. Notably, the performance gap among all six models remains minimal (<0.5% F1 Score difference), suggesting limited returns from increased architectural complexity alone. F1 Loss demonstrates the most balanced performance across architectures, while Focal-Dice-Loss offers superior optimization stability.

The study provides practical guidance for model selection: TransUNet with F1 Loss suits high-accuracy requirements, while simpler attention-enhanced U-Net variants offer cost-effective solutions for large-scale applications. These findings advance intelligent crack detection methodologies and emphasize balancing accuracy with computational efficiency for real-world structural health monitoring.

Unmanned Aerial Vehicles (UAVs) have contributed to a wide range of applications, becoming faster and more sustainable nowadays. However, given the significant increase in the number of UAVs, concerns regarding operational safety have grown.

Autonomous UAV path planning must ensure compliance with safety requirements. This study proposes a real-time path replanning method focused on ensuring compliance with regulations governing UAV operations. Considering no-fly zones (NFZs) defined by both static (buildings) and dynamic (people) obstacles, a low-cost and replicable solution was implemented in four main steps: 3D offline path planning using the A* algorithm and Digital Elevation Models; human detection in UAV imagery using the YOLO11m model; estimation of the person’s 3D coordinates using Monoplotting; and experiments of real-time path replanning. During flight

execution, imagery acquired by the UAV is transmitted to a server and, if a person is detected, path replanning is performed. The replanned route is then sent to the UAV controller to be executed via an SDK-based application. For flights at reduced speeds, the proposed method demonstrated feasibility in a computational environment (replanning time of 2.79 s). Simulated flight execution using the DJI Mobile SDK was successful. However, when relying on data transmission over Wi-Fi, the replanning duration on a local server (17.96 s) remained unsuitable for real-time operations. As future work, alternative solutions should be explored to ensure real-time processing. Despite the challenges, this study contributes by validating the open and free DJI MSDK application for path execution in a simulated environment, integrated with a listener application.

Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal data into a unified representation. This is essential in applications such as construction monitoring, autonomous driving, robotics, and virtual or augmented reality (VR/AR).With the increasing accessibility of point cloud acquisition technologies, such as Light Detection and Ranging (LiDAR) and structured light scanning, along with recent advances in deep learning, the research focus has increasingly shifted towards downstream tasks, particularly point cloud-to-model (PC2Model) registration. While data-driven methods aim to automate this process, they struggle with sparsity, noise, clutter, and occlusions in real-world scans, which limit their performance. To address these challenges, this paper introduces the PC2Model benchmark, a publicly available dataset designed to support the training and evaluation of both classical and data-driven methods. Developed under the leadership of ICWG II/Ib, the PC2Model benchmark adopts a hybrid design that combines simulated point clouds with, in some cases, real-world scans and their corresponding 3D models. Simulated data provide precise ground truth and controlled conditions, while real-world data introduce sensor and environmental artefacts. This design supports robust training and evaluation across domains and enables the systematic analysis of model transferability from simulated to real-world scenarios. The dataset is publicly accessible at: https://zenodo.org/records/17581812.

Kelp forests are vital marine habitats with significant ecological, cultural, and economic importance. These ecosystems, found along coastlines, are susceptible to regional and global stressors (such as coastal development and climate change). This paper presents Satellite-based Kelp Mapping (SKeMa), a novel framework for automatically mapping canopy-forming kelp forests using Sentinel-2 satellite imagery along the British Columbia coast, specifically to support First Nations marine planning for these species. SKeMa employs a deep learning semantic segmentation model, offering an efficient alternative to traditional, labor-intensive, and time-consuming kelp mapping methods. A cross-validation study with independent test sets yields a mean Intersection over Union (IoU) of 0.5326, demonstrating the model’s capability to detect kelp canopies across diverse coastal regions, particularly for larger kelp beds.

The research extended DualSeq, an advanced

machine-learning model for predicting sea surface temperature

(SST), crucial for understanding oceanic ecosystems and climate

patterns. Traditional SST prediction methods typically employ

time-series regressions focusing on nonlinear temporal patterns,

but often overlook vital spatial correlations in SST dynamics,

limiting their accuracy. DualSeq addresses this by integrating

spatial and temporal uncertainty quantification, with a particular

focus on the Arabian Sea. It utilises LSTM and GRU networks to

effectively harness the SEVIRI-IO-SST dataset, which contains

five years of remote-sensing data. A distinctive aspect of DualSeq

is its incorporation of a weighted normalized linear equation,

which significantly improves the accuracy of SST predictions and

enhances the dependability of spatial and temporal uncertainty

assessments. The model stands out in its ability to forecast up to

one month in advance, significantly outperforming others. For 1-

month forecasts, DualSeq shows a remarkable R² value of 0.983,

surpassing the LSTM-attention model by 7.4% and reducing

RMSE and MAE by about 65.4% and 82.4%, respectively.

This performance illustrates DualSeq’s superior capability in

capturing both short-term and long-term uncertainties in SST

forecasting.

This study presents a Station-Centric Geographically weighted Regression (SCGWR) framework for Chlorophyll-a prediction in the optically complex waters of the Gulf of İzmir using Sentinel-2 imagery. Unlike traditional global multiple regression model, the proposed approach calibrates an individual model for each sampling station while using 16 outer Moore-neighbor pixels (range 2) from surrounding stations as independent validation data in the model optimization, thereby preventing adjacency bias and information leakage in performance assessment. Compared to multiple linear regression (MLR) against 20 independent in-situ measurements, SCGWR method offers a robust, reproducible alternative for local-scale water-quality mapping in coastal environments where bio-optical variability is high.

Coastal areas play an important role economically, socially and environmentally due to their many functions.

However, these regions are at risk of erosion, which is further exacerbated by human-driven climate change.

Tracking and monitoring coastal boundaries enable efficient allocation of conservation and protection efforts.

Due to the vast size and complexity of coastal areas, on-site monitoring to track erosion is inefficient.

Artificial intelligence has shown impressive results in segmenting and extracting these boundaries from remote sensing imagery.

Historical remote sensing data make it possible to track long-term erosion but remain challenging due to the coarse resolution of older data.

Our work proposes studying the impact of super-resolution on coastal boundary segmentation using high-resolution imagery.

ESRGAN and SRCNN have proven highly beneficial in improving the quality of coarse-resolution samples, achieving superior performance compared to bicubic interpolation across scaling factors ranging from ×2 to ×12.

ESRGAN super-resolved samples achieved F1-scores ranging from 97.75% to 89.92% for scaling factors ×2 to ×12, while bicubic interpolation achieved between 97.34% and 65.27%.

These improvements demonstrate that SR enhances boundary delineation and robustness across scales.

Our work also explores the applicability of tracking erosion through historical data.

Results demonstrate a coastal boundary change of 0.23 m per year over seven years, which is on par with expected values.

This study introduces a novel framework that integrates a region-aware Full-Waveform Figure Descriptor (FWFD) with a Convolutional Vision Transformer (CvT) for underwater terrain classification using bathymetric LiDAR data. The FWFD converts sequential waveform returns into a multi-directional image-like representation, enabling the preservation of spatial correlations among neighboring laser footprints. By combining convolutional token embedding and self-attention mechanisms, the CvT effectively learns both local and global waveform features. Experiments on a YellowScan full-waveform LiDAR dataset over coastal Australia demonstrate that the proposed FWFD-CvT model achieves 95.55 % overall accuracy under moderate waveform smoothing and exceeds 98 % accuracy for underwater objects. The framework shows robust performance across complex seafloor morphologies and maintains consistency in mixed land-water environments. This research contributes a transferable paradigm for region-aware waveform interpretation and establishes a foundation for extending full-waveform analysis to terrestrial, multispectral, and topographic LiDAR applications requiring fine-scale surface characterization.

Shallow-water cultural heritage occupies a dynamic land-sea interface where coastal erosion, sediment transport, limited visibility and burial processes hinder conventional archaeological investigation. This paper presents an integrated geoinformatics framework for reconstructing the cultural dynamics of coastal and shallow submerged archaeological landscapes in southeastern Crete, Greece. The methodology combines multispectral remote sensing, satellite-derived and in situ bathymetry, UAV and shallow-water photogrammetry, marine geophysics, GIS-based coastal vulnerability, fuzzy logic multi-criteria risk assessment and digital dissemination through augmented reality. The workflow was applied at five representative case studies, including Stomio, Ierapetra harbour, Koufonisi, Chryse and associated coastal sectors. Optical data from Pleiades-1A, PlanetScope, and Sentinel-2A were used for shoreline mapping, feature enhancement, and satellite-derived bathymetry. Geophysical and bathymetric surveys covered more the 4.5 and 10 hectares respectively. UAV photogrammetry produced high resolution orthomosaics, while the proposed experimental Remote Control (RC) boat extends documentation potential to very shallow submerged environments. Integrated interpretation clarified palaeo-shorelines, submerged harbour structures, fish tanks, architectural continuities and archaeological risk hotspots. The results demonstrate a scalable and transferable framework for documenting, interpreting, monitoring, and communicating endangered shallow-water cultural landscapes.

InSAR Norway (InSAR.no) is one of the world’s first fully operational, open-access national ground-motion services. Jointly operated by NGU, NVE and the Norwegian Space Agency, with processing by NORCE on NGU’s high-performance computing cluster, it provides nationwide deformation time-series from Copernicus Sentinel-1 data. The service delivers more than five billion measurement points annually through a public web portal and is widely used for landslide mapping, infrastructure monitoring and climate-related research. It has transformed how Norway identifies and manages unstable slopes, supports early warning and infrastructure safety, and integrates satellite data with in-situ monitoring through networks of snow-protected corner reflectors.

Experience from InSAR Norway directly informed the European Ground Motion Service (EGMS) under the Copernicus Land Monitoring Service, which scales the same operational principles to continental level. EGMS demonstrates that harmonized, validated and open InSAR products can be maintained across national borders.

Building on these achievements, this paper outlines the concept of a Global Ground Motion Service (GGMS)—a federated system providing standardized, GNSS-anchored ground-motion data worldwide. Such a service would combine open satellite data, interoperable processing frameworks and regional capacity-building to support disaster-risk reduction and resilience globally. As the global community invests in disaster-risk reduction, an open GGMS could become one of the most tangible and enduring legacies of the Copernicus era.

Satellite geodesy has become a cornerstone for mapping tectonic deformation, fault activity, and seismic hazard through measurements of surface velocities and strain rates. Yet, in vast regions of diffuse continental deformation, such as the Alpine–Himalayan Belt, observational coverage remains limited. Historically, large-scale studies have relied on sparse GNSS networks, which cannot resolve shorter-wavelength deformation features in many areas. To address this gap, we processed Sentinel-1 radar acquisitions from 2016 to 2024 to generate transnational surface velocity fields and time series at 1 km resolution, spanning more than 11,000 km from southern Europe to eastern China and covering over 20 million km². Our solution integrates more than 220,000 Sentinel 1 SAR images with a newly compiled GNSS dataset, all referenced consistently to the Eurasian frame. From these velocities, we compute horizontal strain rates by taking spatial gradients, providing near-continuous deformation maps across the planet’s largest actively deforming zone. Horizontal motions and strain patterns are primarily tectonic, exhibiting a dual character: strongly localised along major faults yet broadly distributed elsewhere. In contrast, short-wavelength vertical signals largely reflect non-tectonic processes, especially widespread groundwater depletion. These new velocity and strain-rate products constitute foundational datasets, offering a detailed view of continental deformation at a transcontinental scale that feed into the Disaster Risk Management cycle.

The west coast of Canada occupies an active subduction zone and is the host of an often underestimated threat of volcanic eruption. This tectonically active region is the home of 348 known volcanic vents that have been active since the Pleistocene, 54 of which are Holocene in age or younger. The annual probability of any eruption has been estimated at 1/200, while the annual probability of a major explosive eruption has been estimated at 1/3333.

In 2021 the Geological Survey of Canada published a volcanic threat ranking study) which used a threat score assignment methodology developed by the United States Geological Survey. In this study, we describe how the results of this threat ranking guide the acquisition strategy of routine RCM SAR data over the highest threat volcanoes in and around Canada.

We describe the architecture of the fully automated, cloud-based processing system that routinely searches for fresh RCM SAR data, ingests and processes the raw data and displays processed InSAR data on a purpose-built interface for scientific analysis.

With the proliferation of the heavily automated InSAR measurements, human analysis of vast volumes of data becomes challenging. In this research, we also describe the application and performance of an open weight deep learning model trained specifically for the purpose of detecting magmatic unrest in InSAR data. We demonstrate a deformation detection threshold of 6.2 cm and a true positive rate of 0.98 using observations from a real magmatic unrest event in Reykjanes, Iceland through 2023-2024.

We provide updates on the NASA-ISRO NISAR synthetic aperture radar mission and the NASA OPERA project. NISAR launched in June 2025 and began science operations in November 2025. The mission status will be presented and products for different science applications shown. The OPERA project produces four different product streams to support agency information needs, with the Dynamic Surface Water Extent (DSWx), Surface Disturbance (DIST), and Surface Displacement (DISP) products already available, and algorithm development underway for a future Vertical Land Motion product. These are generated from a variety of sensor data, including harmonized Landsat/Sentinel-2, Sentinel-1, NISAR, and SWOT. Examples shown will focus on the DISP products, currently generated from Sentinel-1 data and with a new product line using NISAR data to roll out in early 2027.

Monitoring precursory ground deformation is essential for assessing landslide hazard in regions where hydrological conditions strongly influence surface stability. In Québec’s Saguenay–Lac-Saint-Jean (SLSJ) region, numerous surface failures have occurred in highly sensitive postglacial marine clays, where rainfall, snowmelt, and groundwater fluctuations act as dominant triggers. Although Persistent Scatterer InSAR (PSInSAR) enables regional monitoring of slow ground deformation, its utility for short-term prediction remains limited by the temporal gap between Sentinel-1 acquisitions. This study investigates whether hydrological time-series, when integrated with PSInSAR displacement trends, can be used to forecast the line-of-sight (LOS) displacement observed at the satellite acquisition immediately preceding documented failure events.

A dataset of 102 historical failures (2018–2024) was assembled and paired with 168 Sentinel-1 ascending scenes processed through the StaMPS PSInSAR workflow. Daily precipitation, air temperature, groundwater level, and terrain slope were compiled and temporally synchronized with LOS displacement time series.

An XGBoost regression model was trained to predict the LOS displacement at the subsequent Sentinel-1 acquisition, using an 80/20 train–test split and five-fold cross-validation. Model performance was evaluated using Pearson’s r, MAE, and RMSE. Results show strong predictive skill, with r = 0.82, MAE = 4.36 mm, and RMSE = 6.26 mm. Feature importance analysis highlights the dominant role of recent PSInSAR displacement and groundwater variability. These findings demonstrate the feasibility of integrating hydrological and InSAR time-series to forecast pre-failure surface displacement, supporting the development of satellite-based early warning strategies for hydrologically sensitive terrain.

This research validates social media geospatial tags using Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) data processed on Google Earth Engine, focusing on Hurricane Harvey as a case study. The study addresses critical uncertainties regarding the spatial reliability of crowdsourced disaster information, which has limited integration of social media data into operational disaster management frameworks.

Methodology: The methodology integrated 144,546 geotagged posts from Twitter, Facebook, and Instagram collected during Hurricane Harvey (August 25 - September 3, 2017) with Sentinel-1A SAR imagery processed on the Google Earth Engine cloud platform. InSAR analysis identified 1,247 square kilometers of flooded areas in the Houston metropolitan region. Spatial validation employed buffer zone analysis at 500m, 1km, and 2km distances, with temporal alignment matching social media timestamps to SAR acquisition dates.

Results: Results demonstrate that 68.3% of flood-related social media tags fell within actively flooded areas using 1km buffers, with accuracy increasing to 82.1% within 500m buffers, compared to only 12.7% random expectation. Temporal analysis revealed social media activity peaked 6-18 hours before peak SAR-detected flooding, suggesting potential early warning capabilities. The cloud computing paradigm reduced processing time from weeks to 4-6 hours, enabling near-real-time validation.

Conclusion: This study establishes that validated social media geospatial information can effectively augment satellite-based disaster monitoring systems, particularly during initial response phases when temporal resolution is critical. The integration framework demonstrates operational feasibility for multi-source geospatial data fusion in disaster risk reduction applications.

The paper presents an overview of the European Ground Motion Service (EGMS), a CLMS product that delivers continent-wide, high-resolution measurements of ground motion to users based on Sentinel-1 data. It explains the EGMS architecture, which integrates Persistent and Distributed Scatterer techniques to generate standardised products—Basic, Calibrated, and Ortho—allowing millimetric monitoring of land motion across Europe. The paper emphasises how EGMS fills a critical gap between localised ground measurements and global geodetic frameworks, offering harmonised datasets for hazard assessment, infrastructure management, and policy-making. Applications discussed include subsidence and uplift detection, landslide mapping, and analysis of critical infrastructure.

Looking forward, the paper outlines a potential evolution towards an expansion of the EGMS concept beyond Europe. This would enable standardised, freely accessible deformation data to support global hazard mitigation and climate adaptation. The paper concludes that while technically feasible, a global implementation will require strategic GNSS densification and international cooperation to ensure reliability and equitable access.

Accurate and spatially detailed soil information is essential for sustainable land management, agriculture, and environmental monitoring, yet existing soil maps often lack the resolution required to represent fine-scale soil texture patterns. This study investigates a hierarchical modelling framework that integrates high-resolution remote sensing data, including Sentinel-2 imagery and LiDAR-derived terrain attributes, with soil texture predictions from the provincial SIIGSOL dataset. The approach is evaluated across three contrasting regions in Quebec, eastern Canada, selected for their diverse landscape conditions and soil variability. Two modelling strategies were compared: a model based solely on Sentinel-2 and LiDAR predictors, and a hierarchical model that incorporates SIIGSOL covariates to examine their added value. The findings show that integrating multi-source information improves the representation of soil texture patterns and enhances model stability. This work highlights the potential of hierarchical, multi-scale approaches for producing more accurate digital soil maps. Future efforts will extend this modelling framework across the broader landscape to support high-resolution soil mapping for land management applications.

Crop type mapping is essential for agricultural monitoring, food security assessment, and regional management, yet large-scale operational mapping remains challenging. Reliance on a single modality and the absence of explicit spatio-temporal constraints limit existing methods from fully capturing diverse crop-rotation patterns and phenological trajectories over the growing season. To address this limitation, we propose a multi-source, multi-temporal crop mapping framework. Multi-epoch Sentinel-2 and Sentinel-1 observations are preprocessed in Google Earth Engine to produce co-registered optical and SAR time series, including spectral and vegetation indices as well as radar backscatter descriptors. The proposed model couples cross-sensor interaction with seasonal dynamics: an intermodal Mamba fusion mechanism exploits the complementarity between optical vegetation signals and SAR structural information to strengthen parcel boundaries and reduce sensor-specific artefacts, while a temporal Mamba module explicitly models crop development over time, capturing phenological evolution and differences in the diagnostic value of individual observation dates. Decoding the spatiotemporal representation yields the final crop type map. We evaluate our framework for the Federal State of Brandenburg in Germany, where results demonstrate field-aligned, spatially coherent predictions and robust suppression of speckle- and cloud-induced artifacts, validating joint multi-sensor, multi-temporal modeling for operational crop mapping.

Urban green infrastructure plays a crucial role in supporting ecological connectivity, enhancing climate resilience, and promoting human well-being. As cities densify, maintaining functional green networks increasingly depends on understanding the structural continuity of vegetation within complex urban fabrics. Morphological Spatial Pattern Analysis (MSPA) provides a practical framework for quantifying green infrastructure structure; however, its sensitivity to spatial resolution remains insufficiently examined—particularly at metropolitan scales, where high-resolution data are becoming increasingly available. This study examines the impact of spatial resolution on MSPA outputs for mapping and interpreting urban green connectivity in Miami-Dade County, USA. Two scenarios were compared using 10-m canopy data and 2-m high-resolution canopy data processed across 23 tiles. The workflow integrated vegetation preprocessing, MSPA classification, and quantitative and visual comparisons of structural classes to assess scale effects. Results demonstrate that fine-resolution MSPA (2 m) preserves continuous canopy structures and narrow vegetated corridors that the 10-m analysis tends to fragment or omit. High-resolution outputs provide a more realistic representation of neighborhood-scale connectivity, especially in tree-dense areas such as Coral Gables, while also revealing the computational demands of metropolitan-scale MSPA processing. The findings confirm that MSPA results are inherently scale-dependent and that the choice of resolution critically shapes the interpretation of connectivity. This research provides an operational foundation for incorporating high-resolution morphological analyses into urban resilience planning, nature-based solutions, and socio-ecological equity assessments.

Accurate Land Use and Land Cover (LULC) data are vital for effective land planning and management. This study evaluates the U-Net model for LULC mapping using high-spatial-resolution (2 m) imagery from the WPM sensor on the CBERS 04A satellite. The research focuses on the Servidão River Basin in Rio Claro, Brazil, an urban watershed susceptible to flooding. A pseudo-labeling framework is proposed to reduce reliance on manually annotated training data. Training samples were automatically generated by integrating spectral indices (NDVI, NDWI, SOCI, CI, NISI), Principal Component Analysis, and unsupervised Iso-Cluster classification. Several U-Net configurations were evaluated, with a ResNet-34 backbone with class weighting achieving the highest performance. The model was then retrained using a manually refined reference dataset to enhance the representation of spectrally complex classes. Accuracy assessment resulted in an Overall Accuracy of 0.93, average Precision and Recall of 0.92, and a mean Intersection over Union (IoU) of 0.86. These findings indicate that the proposed pseudo-labeling strategy, combined with a U-Net, offers a robust approach for LULC mapping in complex urban environments using freely available CBERS 04A imagery.

A first-order branch modelling method based on bidirectional searching was proposed, the key steps included skeletonization using local separators, trunk extraction based on path straightness and first-order branch extraction using bidirectional searching. The method was tested on ForestSemantic dataset, and results showed that the extraction precision was 80.29%, and RMSE of the pitch angle estimation was 9.74°, indicating that the method can effectively recover the topological structure of branches.

Long-term and high-resolution terrestrial water storage (TWS) monitoring is critical for water-resource management, climate adaptation, and understanding hydroclimatic variability. Satellite gravimetry missions such as GRACE and GRACE-FO provide unprecedented observations of TWS but are limited by coarse spatial resolution, short observational records, and temporal gaps. This study presents an integrated deep-learning framework for reconstructing and downscaling GRACE/GRACE-FO data to produce century-scale, high-resolution TWS datasets. We apply RecNet and an enhanced RecNet (ERecNet) to reconstruct historical TWS anomalies in the Sudd Wetland, Lake Victoria Basin, and Nile River Basin, leveraging climate variables and lake-level observations. To overcome spatial limitations, we develop DownGAN, a novel generative adversarial network with a high-to-high downscaling strategy, producing fine-scale TWS patterns while maintaining mass consistency. The fusion of reconstruction and downscaling enables detailed, long-term monitoring of wetland dynamics, droughts, and hydroclimatic variability. Reconstructed datasets reveal multi-decadal wetting/drying phases and strong links between TWS fluctuations and climate teleconnections such as ENSO and the Indian Ocean Dipole. This framework advances the application of GRACE/GRACE-FO for climate resilience, ecosystem monitoring, and water-resource management in data-scarce regions, demonstrating the potential of deep learning to extend satellite-based hydrological observations both spatially and temporally.

This study investigates the suitability of Distributed Scatterers (DS) for satellite-based bridge monitoring in very high-resolution (VHR) Synthetic Aperture Radar (SAR) data. While Persistent Scatterer Interferometry (PSI) relies on isolated, temporally stable reflectors, the DS concept extends the analysis to statistically homogeneous areas. In bridge monitoring, however, elevated and narrow structures challenge the assumption of spatial homogeneity due to signal contributions from both the bridge deck and the underlying terrain in side-looking SAR geometry.

Using 23 TerraSAR-X Staring Spotlight acquisitions (September 2022 - September 2023) over two highway bridges near Regensburg, Germany, the study analyses the effects of layover and partial pixel mixing on height correction and deformation estimation. The DS identification is based on statistical homogeneity testing and covariance estimation, with coherence thresholds applied to ensure phase stability.

Results demonstrate that bridge decks exhibit variable coherence depending on surface roughness and illumination geometry. In some cases, overlayed signals from bridge and ground surfaces produce erroneous elevation and deformation values. The analysis highlights the need for careful interpretation of DS results in VHR data and provides insights into the limitations and potential of DS-based InSAR for linear infrastructure monitoring.

Taipei has experienced an important urban development in the recent years with the expansion of its Taipei Mass Rapid system (MRT). This expansion is currently taking place in the Tamsui-Xinyi Line (Red Line) with one new metro station, the Guangci Fengtian Temple Station. This station connects the east part of the Xinyi district as the continuation of the Xiangshan Station. This project extension has been claimed to be one of the most difficult ones in the metro line development due to its complex geological setting going from very soft sediments to hard rock in a few meters. We have employed Sentinel-1 SAR images to measure the tunnel excavation settlement utilizing ascending and descending tracks and estimating vertical and horizontal time series deformations.

Structural health monitoring is essential for building safety. While SAR provides all-weather, non-contact imaging, it is often affected by geometric distortions like layover and foreshortening, making it difficult to extract accurate 3D structural information from complex targets like buildings. Inspired by stereo vision, we propose a stereo SAR mode that acquires two images via a single rotation. By transforming Cartesian to polar coordinates, the disparity is constrained to the angular direction, significantly simplifying the matching process. We derive the nonlinear relationship between height and disparity and apply Newton’s iterative method for accurate 3D reconstruction. Real data collected by a millimetre-wave radar system validate the effectiveness of the proposed approach.

3D city models have become an important piece of geoinformation. They are available in different Levels of Detail (LoD), which determine the amount of complexity provided in the model. LoD1 city models represent simple prismatic building volumes and are typically produced by means of remote sensing. In this article, we investigate the possibility for country-wide reconstruction of LoD1 city models from TanDEM-X intensity images by utilizing deep learning-based single-image height and building footprint reconstruction. As study area, we use the land surface of the country of Denmark. Our results show the general potential of this AI-based approach of country-wide city model reconstruction, which can serve as a data-efficient pipeline that is particularly well-suited in time-critical scenarios or for the exploitation of archive imagery of satellite missions with global data coverage.

Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure millimetre-level deformation of bridges and other struc-tures. However, retrieving multi-dimensional displacements of a bridge and integrating these measurements with structural stress for coupled analysis remains a major challenge. To tackle this issue, we propose an integrated framework and demonstrate its application on the Hutiaohe extra-large bridge in Guizhou Province. First, a two-dimensional E-PS-InSAR time-series processing chain is de-veloped to derive the bridge’s bi-directional deformation. Next, structural temperatures are obtained through the ANUSPLIN interpo-lation scheme, allowing the accurate isolation of the thermal response. Finally, the finite-element model (FEM) of the bridge is con-structed to interpret the observed deformation and thermal signatures within the structural context. The results show that, compared to conventional InSAR approaches, the proposed framework yields a richer set of insights by conducting a joint analysis mul-ti-dimensional deformation, structural behavior and thermal effects.

Monitoring linear infrastructures such as railways and highways with Multitemporal Interferometric Synthetic Aperture Radar (MTInSAR) requires to identify spatial displacement gradients to assess and mitigate the related hazard. During conventional MTInSAR, the majority of the processed pixels are not directly relevant to the linear infrastructure. However, these pixels are required to aid the phase unwrapping and to remove the atmospheric phase contribution. To overcome this limitation, we propose a new method that directly estimates the spatial gradient from the Synthetic Aperture Radar (SAR) images solely along the linear infrastructure avoiding costly phase unwrapping, error propagation from pixels outside the linear infrastructure and atmospheric filtering. Our experiments based on high and medium resolution images from TerraSAR-X and Sentinel-1, respectively, demonstrate that the estimated spatial gradients agree well with the MTInSAR results with a maximum Root Mean Square Error (RMSE) of 3.5 mm/year. Applying our method on Sentinel-1 images enables computationally efficient monitoring of linear infrastructures exploiting the wide area coverage and availability of the SAR images.

Jamming and spoofing of GNSS signals have become common practice in war zones and areas of political tension. The unavailability of reliable GNSS signals has a major impact on mapping services. Airborne laser scanning is one type of aerial survey that depends on GNSS. In this presentation, we propose a concept for airborne laser scanning surveys without using GNSS. We also present the results of an initial feasibility study.

Accurate cadastral maps are essential for effective land administration, supporting tenure security, land management, and socio-

economic planning. Automating cadastral boundary extraction can accelerate mapping in regions with incomplete or absent cadas-

tral information, but deploying pretrained models in data-scarce areas is challenging due to limited reference data and heterogeneous

landscapes. In this study, we investigate cross-region transfer learning for delineating cadastral boundaries using high-resolution

aerial imagery. We employ CadNet, a U-shaped deep learning model with a Swin Transformer backbone pretrained on the Dutch

CadastreVision dataset, and fine-tune it using Polish cadastral reference data selected for landscape similarity to a data-scarce region

in northern Moldova. Evaluation on Moldovan test tiles demonstrates substantial quantitative improvements: recall for visually dis-

cernible boundaries increases from 0.310 to 0.624, total vector-based discrepancy via Normalized Discrepant Area decreases from

7.898 to 7.051. Qualitatively, fine-tuning produces more continuous and coherent boundaries, recovers interior parcel divisions, and

better aligns predicted parcel structures with ground truth, compared to the pretrained model, which generates fragmented and in-

complete boundaries. These results highlight the importance of landscape similarity and reference data quality for transfer learning

and demonstrate a scalable framework for automated cadastral mapping in regions with similar landscape characteristics.

This study presents Siemens star studies in Norway, Finland, and Denmark during 2023-2025. The preliminary results demonstrate a significant and expected difference between GSD and GRD, highlighting that the GRD is a critical parameter when planning and procuring aerial imagery services. GRD relates to the smallest objects that can be reliably mapped. Incorporating GRD into planning ensures that expectations better match the final outcome. The study provides valuable insight into the practical use of Siemens star considering size, frequency, design, material selection, including comparisons between Bayer pattern and pan-sharpened sensors. The Nordic countries have different strategies for evaluating GSD considering prequalification, national calibration fields and field installations on individual projects. This study provides an overall assessment of the different approaches. The project aims to establish common requirements and methodologies for aerial image quality assessment, ultimately contributing to a European-wide GRD based resolution standard

Benefiting of EU funds coming from National Plan for Recovery and Resilience after the covid-19 pandemic, Italian Ministry for the Environment and Energy Security, in coordination with Istituto Geografico Militare and Istituto Nazionale di Geofisica e Vulcanologia, is currently implementing a national project for the acquisition and processing of airborne LiDAR and gravimetric data covering the entire Italian territory. The goal is to overcome the heterogeneity of existing digital terrain and surface models and gravimetric dataset, which suffer from inconsistencies in spatial coverage, temporal epoch, accuracy, and metadata completeness. The project will produce homogeneous, high-resolution Digital Terrain and Surface Models (DTM and DSM) and a new airborne gravimetric database, enabling to estimate a refined gravimetric geoid and significantly improving the Italian geospatial reference infrastructure. All the collected data and realized products will be publicly available. The main features of the project, and a selection of the already available results are hereafter presented.

High-altitude aerial image national mosaics often exhibit visible colour and tone differences caused by atmospheric variability, illumination changes, sensor differences and post-processing workflows. These radiometric inconsistencies negatively influence both visual quality and the comparability of image data across sensors, time and campaigns. This work presents an empirical two-step colour adjustment and radiometric normalisation method for imagery acquired from 2000–3000~m altitude using a large multi-colour ground target designed to provide stable, spatially robust reference statistics. Field reflectance values are measured with a handheld spectrometer and converted to CIELAB coordinates. A global 3D similarity (Helmert) transform aligns measured image colours to ground-truth CIELAB values, followed by local residual chromatic correction using inverse distance weighting. Experiments on aerial datasets demonstrate that the method significantly reduces colour discrepancies at the calibration site.

Frequent cloud cover is a common impediment deterring many countries from employing optical earth observation data for the purposes of national map updates. A decision-level data fusion approach allows the use of satellite imagery in such locations and therefore has potential to assist in this task. In this study we test the use of cloud penetrating Sentinel-1 to enhance the delineation of impervious surfaces from other land cover types, impervious surfaces being a key component of hydro-climatological models in urban and semi-urbanised areas. Using machine learning techniques and leveraging the full Copernicus archive in the Google Earth Engine (GEE) platform, a post-classification change detection approach was developed to assess impervious surface expansion between 2017 and 2023 across the urban centre of Dublin, Ireland. Image classification, conducted using a random forest classifier, achieved overall accuracies of 93% and 91% and kappa coefficients of 0.91 and 0.89 for 2017 and 2023 data, respectively. The addition of multispectral and RADAR indices such as NDVI, NDBI and PRISI was tested and proved generally effective, but showed limitations in areas adjacent to the coast and inland water bodies, with indications of confusion between land cover types. The inclusion of NDWI in data fusion was shown to help differentiate waterbodies from impervious surfaces, particularly highlighting the importance of integrating a water-specific index. NDVI also outperformed other indices in feature importance, though PRISI was shown to helpfully cluster impervious surfaces

Digital twins (DTs) are digital representations of physical entities with data connections synchronising the physical and digital states. While DTs originated in manufacturing and aerospace, they are increasingly applied at geographic scales addressing urban issues. As a result, DTs must utilise geographic information (GI) to represent the built environment, though this is often an implicit aspect. Public sector geographic information (PSGI), typically produced by National Mapping and Cadastral Agencies (NMCA) is a particular type of GI that serves as an authoritative, foundational component to geospatial applications. However, the value of this PSGI as foundation component of DTs is not well understood. Existing GI valuation methodologies do not account for the unique characteristics of foundational PSGI, or its role within DTs , leaving NMCAs unable to justify investment, and adapt their contributions, to emerging DTs. To address this gap, this study applies Jabareen's (2009) conceptual framework analysis methodology to define what value means in the context of PSGI in DTs. The analysis identifies seven value enablers and five value dimensions that characterise PSGI value in DTs and provide the basis for future quantitative valuation methodologies. These concepts are integrated through an urban infrastructure DT example and synthesised through boundary case analysis. The resulting conceptual understanding provides a foundation for NMCAs to systematically articulate and evidence their contributions to DTs.

Digital Twins of Territories (DTTs) are increasingly adopted by municipalities to support ecological transition, crisis resilience, and participatory decision-making. Designing a DTT that fits local needs requires engineers to combine multiple areas of expertise (data discovery, integration, modeling, visualization, and stakeholder interaction) while working with heterogeneous geospatial datasets of varying quality. Nation-wide DTT frameworks aim to assist these efforts, yet they currently lack mechanisms to consolidate the expertise produced during local DTT developments.

This paper introduces dttrecipe, a model designed to capture, structure, and share DTT engineers' feedback and decision-making processes. Building on the prov, wfdesc and wfprov ontologies, and inspired by the OGC Geospatial User Feedback standard, dttrecipe formalizes the description of territorial stakes, data workflows, encountered problems, and the rationale behind design choices. It supports both complete and partial workflow descriptions, encouraging collaboration, reproducibility, and cross-territorial knowledge reuse.

The model is qualitatively evaluated via a case study focused on bicycle-mobility planning and citizen engagement in a rural city. The resulting recipe highlights recurrent categories of DTT engineering challenges, including data discoverability and usability issues, multi-source misalignment, documentation accessibility, and limited local expertise. Explicit documentation of these challenges shows how engineers' often implicit expertise can be converted into reusable knowledge for other territories facing similar constraints.

The work shows that structured documentation of DTT engineering practices can strengthen national DTT frameworks by improving interoperability and enabling efficient knowledge transfer. Future work will address querying mechanisms and evaluate the reuse of shared recipes at scale.

C-band scatterometers have been collecting radar backscatter data since 1991, providing valuable long-term records for environmental monitoring applications such as soil moisture and vegetation dynamics. However, differences in sensor calibration between missions introduce biases that compromise the continuity of these data records. This paper presents the practical implementation and adaptation of Reimer's (2014) rainforest-based inter-calibration approach for ESA's ERS satellites (ESCAT) and MetOp/ASCAT instruments. We implement the method as a modern, open-source Python framework and apply it to the newly complete ERS data record (including ERS-1 data not available in the original study). The resulting calibrated backscatter data record will enable improved long-term monitoring of land surface dynamics with reduced mission-to-mission variability in bias and slope response over incidence angle.

Improved availability and quality of high-resolution satellite imagery allow for reliable airplane detection. Yet, fine-grained classification, especially of commercial airliners, remains a formidable challenge. Besides common difficulties, such as varying image

artifacts and occlusions, the main challenge lies in the strong visual similarity between airliner families. This paper presents a geometry-aware classification that enhances oriented object detectors by integrating absolute measures and geometric features – fuselage length, wingspan, wing sweep angle, engine count, and fuselage width – in the form of priors into a Bayesian maximum a posteriori (MAP) estimation. The proposed pipeline is detector-agnostic by updating class posteriors without retraining the main detector. On the Gaofen Challenge dataset, it results in consistent improvements based on untuned baseline detectors, which outperform the top scores of the sophisticated fine-tuned models. An oracle experiment reveals the potential of the approach with an upper limit of the overall mean Average Precision of up to 0.96 and 0.98 for Gaofen and SuperView data, respectively. Furthermore, the impact of the employed geometric attributes is quantitatively evaluated.

IFC models store detailed building information, but their complex schema and deeply nested property sets make querying difficult for non-expert users and challenging for large language models (LLMs) to handle directly. Current LLM-based approaches are inefficient because prompts often include entire IFC schemas, many properties of which are irrelevant to the user’s query, leading to higher inference costs and potential errors. This paper presents Query2Property, a semantic retrieval system that maps natural language queries to the most relevant IFC properties. By embedding both property descriptions and user queries in a shared vector space, the system retrieves contextually relevant properties for dynamic and concise prompt construction in LLM-driven workflows. Evaluation on 55 representative BIM queries achieves a top-1 accuracy of 87.3% and top-3 accuracy of 100%, demonstrating effective alignment with user intent. Query2Property simplifies LLM-based workflows over BIM data, supporting semantic search and natural language exploration of complex building information.

Semantically rich 3D city models play a vital role in a variety of applications, such as urban planning. Enhancing these models with currently unavailable attributes, such as building storey numbers, can unlock new opportunities to address pressing challenges, including sustainable urban development. In this work, we present an end-to-end pipeline for the automatic estimation of the number of storeys to semantically enrich 3D city models. We employ volunteered geographic information street-view imagery

from Mapillary, using a COCO-pretrained object detection model to identify windows in façade images as key visual indicators for inferring building storey counts. Our detection pipeline, based on the YOLOv3 architecture, estimates storey numbers using an ensemble of clustering methods including Gaussian Mixtures and DBSCAN and enables the automatic augmentation of CityGML-based 3D city models by filling in missing attributes. This enrichment supports advanced applications, such as assessing building-scale energy demand, evaluating vertical urban growth patterns or population density estimations. We validated the feasibility of our approach with unfiltered Mapillary and applied it to a district in the city of Heidelberg, Germany. The paper also includes a detailed discussion of learning process quality, integration workflows, and visualization of the enriched 3D city model. The developed code is available at: https://github.com/hcu-cml/citydb-buildingstoreys-ai.

This research addresses the need for accurate updates to the seabed datasets in coastal areas under environmental and human pressure. It uses Google Earth Engine (GEE) to develop a cloud-based application for Satellite-Derived Bathymetry (SDB) of the Irish bays using Sentinel-2 and Landsat-8 imagery. For the validation, the OPW Pilot Coastal Monitoring and INFOMAR datasets were used. The research refines semi-empirical algorithms and introduces an Earth Engine App (EEA) using the JavaScript API specifically tailored for and non-specialist public use. The methodology employed included pre-selecting high-quality satellite images based on the higher R-squared and lower RMSE to ensure reliability and better performance. In the initial phase, 18 bays were assessed, and the results showed that five bays (Dublin, Dungarvan, Portrane, Rosses, and Tramore) performed better across the evaluated metrics. he development and use of this application support a wide range of marine applications, especially for capacity building, as part of the pilot research led by Maynooth University and Geological Survey Ireland (GSI).

Accurate estimation of methane fluxes across Arctic wetlands is essential for understanding carbon–climate feedbacks, yet remains difficult due to sparse ground measurements, strong spatial heterogeneity, and the coarse resolution of most existing bottom-up inventories. To address these limitations, we develop a high-resolution methane flux modeling framework that integrates multisensor Earth observation data with a geofoundational deep-learning approach. The study leverages 30 m Harmonized Landsat–Sentinel (HLS) imagery, together with environmental predictors from SMAP and ERA5, and daily eddy-covariance methane flux measurements from Arctic sites after 2015. Following data filtering and quality control, the dataset comprises more than 7,600 daily observations from 45 wetland sites across northern high latitudes. A hybrid model architecture is constructed by combining the Prithvi geospatial foundation model for HLS feature extraction with a lightweight feature-wise attention encoder processing 48 auxiliary environmental variables. Fused latent representations are used to predict daily methane flux at 30 m resolution. The model demonstrates strong performance on an independent test set, capturing key spatial and temporal patterns of methane emissions. By enabling fine-scale flux estimation far beyond the resolution of conventional 0.1°–0.5° inventories, the framework offers new opportunities for detailed Arctic methane monitoring and improved characterization of wetland-driven emissions.

To plan nature restoration of fluvial corridors on a national level an inventory of existing man-made levees is mandatory. We suggest an automatic method for a river-wise extraction of levees from a high resolution terrain model based on profiles perpendicular to the river axis. In this course we present a method to cover corridors with non overlapping profiles with a given maximum distance. Levee detection is based on a mathematical formulation of the protective function of levees. In an evaluation of 150 km river length distributed over nine different rivers in Austria the method detected 98% of manually extracted levees, and 68% of their length.

Temperatures in urban areas are rising due to the climate change. Together with increasing urbanization and densification reducing cooling green spaces in cities this leads to so called urban heat islands (UHI) with increased surface- and air-temperatures in urban areas relatively to the surrounding areas. Since high temperatures are the reason for many exceed deaths municipalities are forced to protect their citizens. Satellite earth observation allows to monitor the development of urban heat islands to warn inhabitants early from dangerous heat. An other important way is increasing the resilience of cities to heat waves. For this we developed a simple but efficient method for the simulation of urban surface- and air-temperatures from single very high resolution stereo satellite images. In this paper we present the improved workflow for the simulation of urban temperatures together with the calibration and validation. Further we compare the results to in-situ-measurements in the city of Memmingen in southern Germany, to LandSat thermal mapper imagery and existing works on urban heat islands. Additionally we show how modifying the digital twin e.g. by adding trees or water areas allow the simulation of different scenarios to support decision-makers on their path towards resilient cities.

Scandinavia is facing climate change, with mean temperatures projected to rise by 2-4°C. To prepare Swedish forests for this challenge, the Swedish tree breeding program aims to selects trees adapted to a range of biotic and abiotic conditions. Key variables in this selection process include spring phenology, damage, and overall tree vitality.

Traditionally, these data have been collected through manual field assessments, a resource-intensive approach that constrains both the number of trees that can be evaluated and the frequency of measurements. Remote sensing offers an alternative: high-resolution multispectral drone imaging enables the scoring of greater numbers of trees in less time, captures multiple data points across the growing season, and reduces the risk of human error through algorithmic measurement.

This project aims to develop methods suitable for integration into the Swedish tree breeding program by using multispectral drone imagery to assess spring phenology, shoot damage, and vitality in young Norway Spruce. Field campaigns were conducted during spring 2023 and 2024. Bud flush is modeled from spectral values of tree crowns, using manual assessments of a subset of trees as training data. To capture the full progression of bud flush at high temporal resolution, images were acquired before the vegetation season and up to twice weekly during the period of most rapid development. The same modeling framework is applied to assess damage and vitality.

Antarctic ice shelves regulate ice sheet mass balance through their "buttressing effect", with major implications for global sea level rise. This study focuses on the Nansen Ice Shelf in Victoria Land, East Antarctica, which exhibits complex topography and sensitivity to environmental changes. Previous research has primarily centered on its significant collapse event in 2016; however, systematic evolutionary patterns over longer timescales remain unclear. This study integrates multi-source remote sensing observations from 1948 to 2025 to systematically reconstruct changes in the Nansen Ice Shelf's geometric characteristics (crevasse width, area) and dynamic parameters (ice flow velocity). Findings reveal distinct activity differences between the northern and southern regions of the ice shelf, closely linked to their respective boundary conditions and structural features.

Flood mapping is one of the most important applications of Synthetic Aperture Radar (SAR) because it can monitor the earth's surface under all-weather, day-and-night conditions. While SAR intensity has been widely used for flood mapping, the potential and added value of interferometric coherence, especially its temporal behavior in different environments, remains mostly unexplored. In this study, we assess the potential and added value of interferometric coherence from Sentinel-1 time series for flood mapping in three contrasting regions: the urban area of Valencia (Spain), the arid region of Sistan and Baluchestan (Iran), and the agricultural area of Hannover (Germany). Our analysis of multi-temporal coherence shows that coherence provides clear flood indicators in arid regions through strong temporal decorrelation, but its performance is less reliable in vegetated and urban areas. In agricultural regions, pre-flood (baseline) coherence is inherently low due to vegetation phenology and temporal decorrelation, making any additional decrease due to flood inundation often indistinguishable. In urban areas, coherence generally remains stable, with only slight decreases observed in specific cases; therefore, the detectability of flooded areas using coherence-based approaches is limited in both agricultural and urban environments. In contrast, coherence in arid regions is high before flooding and drops significantly during flood events, making floods easy to detect in such regions. These findings demonstrate that, for flood mapping, interferometric coherence is a valuable but environment-dependent indicator, with the highest benefit seen in arid regions where intensity-based methods are limited.

Arctic sea ice thickness has been declining over recent decades due to climate change, making accurate prediction increasingly critical for environmental monitoring and climate modeling. Microwave remote sensing combined with machine learning has emerged as a promising approach for estimating sea ice thickness. This study investigates the prediction of lab-grown sea ice thickness, ranging from 27 to 47 cm, using time-series backscatter data collected from surface-based Ku- and C-band scatterometers in three polarizations (VV, HH, and HV). A Random Forest model was applied to the time series, incorporating Normalized Radar Cross-Section (NRCS) values and statistical features (mean and standard deviation) across various temporal variables (lead and lag times). The model achieved high prediction accuracy, with the lowest error recorded at RMSE = 0.03 cm. Feature importance analysis using the Permutation Importance method revealed that co-polarized C-band features (C-VV and C-HH) were the most influential in predicting sea ice thickness. These findings underscore the potential of integrating microwave remote sensing with Random Forest models to enhance sea ice thickness prediction and provide valuable insights for future research and real-time monitoring in Arctic regions.

Operational flood monitoring demands both accurate extent delineation and quantitative depth estimation, yet existing research addresses these objectives separately. This study presents an integrated SAR-to-depth framework combining state space model segmentation with DEM-based geometric depth estimation to deliver comprehensive flood intelligence from Sentinel-1 SAR imagery and digital elevation models. We propose CS-Mamba, a hierarchical U-Net architecture incorporating selective state space

mechanisms, achieving 79.79% mean IoU on 10 European flood events from the KuroSiwo benchmark while surpassing CNN baselines and outperforming RSMamba by 7.37 percentage points. Test performance exceeding validation confirms robust cross-event generalization to unseen disasters. Controlled experiments establish that deep learning predictions achieve sufficient accuracy for operational depth estimation, with CS-Mamba flood masks showing ±2% agreement with reference annotations across four global DEMs despite conservative extent delineation. This agreement enables integrated pipelines without manual annotation, while systematic DEM comparison identifies Copernicus and MERIT as optimal choices. The complete framework delivers three-class flood masks and pixel-wise depth maps at operational resolution, bridging the traditional gap between extent mapping and quantitative assessment for emergency response.

This contribution introduces TGSD-Net, a temporal variation-guided self-supervised network designed to improve despeckling of polarimetric SAR (PolSAR) imagery without the need for clean reference data. The method leverages consecutive multi-temporal observations to create pseudo training pairs and incorporates a lightweight temporal change detection prior, allowing the network to exploit temporal redundancy while remaining robust to land-cover variations. TGSD-Net further integrates auxiliary polarimetric decomposition features and a spatiotemporal information fusion module to enhance structural and scattering representations. The approach is tailored for multi-temporal SAR scenarios, where speckle, temporal variation, and heterogeneous land-cover types pose significant challenges. Experiments on real PolSAR datasets show that TGSD-Net achieves strong noise suppression while preserving edges, textures, and physical scattering properties. The results demonstrate the potential of self-supervised temporal learning to advance PolSAR image restoration and support downstream remote sensing applications.

Current Remote Sensing applications demand multi-source, multi-sensor data fusion. Multi-source, multi-sensor data fusion provides useful information integrated for quick and better interpretation, understanding and effective decision-making. Data fusion of Synthetic Aperture Radar (SAR) data of Earth Observation Satellite-04 (EOS-04) and Optical Multispectral (MX) data of Sentinel-2 are current topic of interest in this paper. SAR and Optical MX which includes active and passive remote sensing technologies belong to different mechanisms of wave interaction due to widely separated and non-overlapping regions of the electromagnetic spectrum. In this paper, a novel approach to the re-implementation of Wavelet, Brovey, Fast Intensity Hue Saturation (FIHS), Frequency filtering, and Pure pixel data fusion methods is presented. The presented novel approach emphasises modulation-based fusion technique with proper normalization and scaling of both the input datasets. Fusion results of presented fusion methods are evaluated visually as well as quantitatively with quality metrics. The quality metrics demonstrate the ability of the presented novel approach to fuse optical spectral information into SAR data effectively to generate improved high-resolution SAR-coloured fused products.