Conference Agenda

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper presents a workflow comprising domain adaptation and classification.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This study aims to address this gap by:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

elevation monitoring.

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

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

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

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

A centroid regression mechanism enabling precise geolocation correction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

foundation for the current application and study.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

concentration and evaporation rate.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Surface Water and Ocean Topography (SWOT) mission

represents a major advance in Earth observation by providing the

first global two-dimensional measurements of surface water

extent and elevation. Its potential for hydrology, climate

monitoring, and water resource management is widely

recognized; however, recent studies indicate that its performance

varies across hydrological contexts.

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

water monitoring based on a synthesis of published validation

studies, simulation experiments, and case applications. To

support a structured interpretation of these findings, a Strategic

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

analytical framework that organizes the evaluation across four

components: strengths, limitations, opportunities, and risks,

enabling a systematic comparison of SWOT performance under

different environmental and observational conditions (Figure 1).

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

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

wetlands and narrow channels, retrieval errors increase

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

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

heterogeneity, including shoreline complexity and wind-induced

surface roughness, further increases uncertainty in elevation

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

reduce water–land separability and limit effective pixel

availability (Frasson et al., 2021).

The SAF highlights performance variability and identifies the

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

Scope) in improving the reliability of SWOT-based inland

water monitoring

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

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

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

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

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

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

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

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

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

test

test

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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