Conference Agenda

This presentation introduces a digital twin and immersive VR system that reconstructs the “Hidden Foot” of Borobudur Temple in Central Java, Indonesia—an encased architectural layer containing 160 narrative bas-relief panels that cannot be directly observed today. The system integrates multi-source 3D point cloud data acquired via UAV and ground-level imaging with deep-learning–based 3D reconstructions of reliefs from historical monocular photographs and semantic segmentation results from previous work.

A custom passage model simulates the narrow gap between the original relief-bearing wall and the surrounding protective masonry, allowing users wearing a head-mounted display to virtually enter and walk through this otherwise inaccessible space. Within the VR environment, users can switch between photorealistic relief rendering and color-coded semantic overlays to support both immersive appreciation and analytical interpretation. A torch-based Level of Detail (LoD) strategy, driven by dynamic illumination, maintains high visual fidelity near the user while reducing rendering load for distant geometry, ensuring stable frame rates suitable for comfortable VR exploration.

A small user study indicates high ratings for visual realism, immersion, and the educational clarity of semantic overlays and gaze-triggered text annotations, highlighting the potential of this approach for research, documentation, and public engagement with hidden cultural heritage.

Three-dimensional (3D) scanning is widely used to preserve cultural heritage as large-scale point clouds. While these datasets contain rich geometric information, transparent visualization of such massive point clouds often suffers from visual clutter and reduced clarity, particularly when both external and internal structures are involved. Previous work resolved the problem of normal orientations, laying the foundation for robust shading in transparent visualization. Building on this foundation, this paper introduces a novel method of adaptive opacity control for region highlighting, which interprets shading as a distribution of opacity. By adjusting the lighting direction, effective opacity can be locally controlled without modifying the original point cloud data. This mechanism enables selective highlighting of user-specified regions, enhances the visibility of complex structures, while also allowing interactive dynamic shading by continuously changing the lighting direction. The effectiveness of the proposed method is demonstrated using culturally significant heritage point clouds, including UNESCO World Heritage sites, where intricate internal structures can be more clearly analyzed. Beyond cultural heritage, the proposed method is also applicable to modern architectural and other large-scale 3D scanned objects with similarly complex forms.

The concept of Digital Twin has attracted growing interest within research communities, including heritage conservation, in recent years. It combines detailed geometric documentation, real-time monitoring, and semantic information to create dynamic digital replicas of historic buildings. This paper presents the results of a scoping review of 204 peer-reviewed studies published between 2020 and 2025. The aim is to identify the main technologies, devices, and methods used to develop a Digital Twin for heritage buildings. The review reveals that terrestrial laser scanning (TLS), UAV photogrammetry, BIM, and IoT sensor networks form the core technological base. It also highlights the growing use of artificial intelligence for automated defect detection, predictive maintenance, and semantic processing. Based on the reviewed literature, the paper introduces a six-stage workflow for building a heritage Digital Twin, covering baseline documentation, static reality capture, semantic modelling, sensor integration, data fusion, and operational use. The findings show a clear shift from static 3D documentation toward dynamic, data-rich systems that support continuous monitoring and more informed decision-making. However, the review also identifies major challenges, including limited interoperability, complex data integration, incomplete AI validation, and long-term digital preservation issues. Overall, the study outlines the current state of Digital Twin technologies in architectural heritage and identifies key areas that require further research to support reliable and sustainable applications.

In the current context of energy and climate transition, the preventive conservation of historic buildings is particularly important due to their impact on architecture and works of art. Establishing the correlation between environmental variables and the condition of artworks in situ requires comprehensive and individualized monitoring, allowing for an understanding of cause-and-effect mechanisms. To address this challenge, the EPICO method provides a systematic framework for assessing deterioration risks in palace-museums through multi-scale monitoring and correlation between environmental parameters and object condition. The aim of the proposed topic is to enhance this decision-making tool with the creation of digital twins. These digital twins being fed with three-dimensional hyperspectral and LiDAR mapping of spaces and objects.

Urban digital twin systems require 3D city representations that reconcile semantic structure, geometric reliability, simulation capability, and photorealistic real-time rendering. Existing approaches typically prioritize a single modelling paradigm, limiting their capacity to simultaneously support analytical and visualization demands. CityGML ensures standardized semantics and topological consistency but often lacks detailed surface realism. Surface-based semantic mesh models preserve geometric detail suitable for environmental simulations but provide limited hierarchical semantic organization. In contrast, neural radiance-field approaches, such as 3D Gaussian Splatting (3DGS), enable photorealistic rendering at interactive frame rates but do not explicitly encode topology or structured semantics. This study establishes a structured comparative framework linking LiDAR-derived CityGML, triangle-based semantic mesh, 3D Gaussian Splatting, and Triangle Splatting within a unified urban modelling workflow. UAV-based data acquired using a multirotor platform with a DJI ZENMUSE L2 sensor serve as the geometric backbone for reconstructing CityGML LoD1-LoD2 models. The semantic model is transformed into a textured triangular mesh to provide a geometry-consistent baseline, while radiance-based models are generated from the same imagery using multiple 3DGS implementations and a triangle splatting framework. Comparative evaluation investigates geometric coherence, semantic preservation, and radiance consistency to identify structural correspondences across the representations. Rather than treating them as competing alternatives, the results reveal complementary modelling layers that can be systematically mapped. Based on these findings, the paper formulates a conceptual foundation for a unified 3D urban model capable of transforming consistently into semantic-structured, surface-based, and radiance-based representations, enabling adaptive and extensible urban digital twin systems.

Persistent Scatterer Interferometry (PSI) provides valuable information on ground and structural changes, particularly in dynamic urban environments. At the same time, urban digital twins (UDTs), as detailed three-dimensional representations of cities, are increasingly used for monitoring and analysis. However, the effective integration of results of PSI processing named Point Scatterers (PSs) into such frameworks remains challenging due to the limited positional accuracy of PSs, despite the high precision of displacement estimates. This study investigates a methodology for integrating PSI data from the European Ground Motion Service (EGMS) with airborne laser scanning (ALS) data and Level of Detail 2 (LoD2) building models to improve the connection of PSs with real-world objects. Three integration variants were analysed, differing in the reference datasets used for linking: (i) ALS point cloud, (ii) point cloud derived from LoD2 models and digital terrain model (DTM), and (iii) a combined approach integrating ALS and LoD2 representations. The results demonstrate that the combined approach yields the highest performance, achieving up to 88% of successfully linked PSs, compared to 70.4% and 80.3% for the ALS-only and LoD2-based approaches, respectively. The findings indicate that LoD2 models provide sufficient geometric detail for PS linking, despite lacking fine-scale building elements. Their use improves data completeness, particularly on building facades, where ALS data are often sparse or missing. The proposed methodology confirms the applicability of EGMS products as a valuable data source for 3D geoportals and urban digital twins, supporting advanced spatial analyses in complex environments.

Integrated Water Service (IWS), which combines water supply and wastewater treatment, requires complex geometric and semantic management. Building Information Modelling (BIM) and Geographic Information Systems (GIS) are the two main geospatial technologies involved in this field. In very simple terms, BIM allows to have 3D models with detailed geometric and semantic information, and GIS permits to geolocate and manage the models in the territory. To facilitate the integration of these two systems, we propose to manage the BIM models through a standardised relational database. In the BIM world, relational databases are not yet widely used, but the technology is already available. For example, ifcSQL is an encoding of the Industry Foundation Classes (IFC) data model for a relational database. This article proposes an extension of the ifcSQL database with the added possibility to store the georeferenced explicit geometries of the IFC models. Additionally, we present a prototype to make such IFC-based data available via QGIS. In this way, a user can interact with BIM data using open GIS technologies. As a result, it is possible to visualise the models in 2D and 3D, and to perform queries on their attributes. A set of real-world case studies has served as testing ground to develop the functionalities that allow for the interaction with the BIM models via QGIS. Such test cases originate from interviews with a company that manages IWS in Northeast Italy.

Early Wildfire detection using AI remains challenging in environmental monitoring, particularly when the approach should be flexible enough to handle different sensors and different landscapes. This study presents a multi-stage deep learning framework for real-time smoke and fire detection using imagery from fixed tower cameras and UAVs. The proposed system employs YOLOv11 as the primary detection model for high-speed inference, com-plemented by Faster R-CNN for precision benchmarking and cross-architecture evaluation. Together, these models support an in-depth analysis of detection accuracy, robust-ness, and computational efficiency across diverse envi-ronmental conditions.

An end-to-end pipeline has been developed, integrating real-time image acquisition, asynchronous message han-dling through RabbitMQ, and performance logging via InfluxDB, enabling continuous model evaluation under near-operational conditions. Experimental results indicate that while YOLOv11 achieves high frame-rate perfor-mance and strong detection capability, it remains suscepti-ble to false positives in visually ambiguous scenarios such as haze, fog, or low-contrast backgrounds, where contex-tual patterns closely resemble smoke. Faster R-CNN serves as a complimentary reference to quantify localiza-tion accuracy and analyse error propagation, facilitating threshold tuning and model interpretability.

The presented framework bridges the gap between aca-demic model development and field-deployable fire sur-veillance systems. It establishes a reproducible, scalable foundation for real-time decision support in forest watch-tower networks and autonomous UAV missions aimed at early wildfire detection and response

Wildfires induce significant changes in forest structure and the surface reflectance characteristics. This study evaluated the effectiveness of using airborne LiDAR Intensity data to delineate surface fire burn areas in wildfires. We extracted ground returns from both coniferous and deciduous forests and conducted qualitative assessment of Intensity through Intensity images, as well as statistical evaluation using the non-parametric Mann–Whitney U test to compare burned and unburned areas. We compared the median and standard deviation of Intensity at a 10-m mesh scale, calculating standard deviation at a finer 0.5-m mesh resolution. The results revealed significant differences between the two groups. As a result, a significant difference was observed between the two groups. The effect size r for the median in deciduous forests ranged from 0.55 to 0.84, while the effect size r for the standard deviation in coniferous forests ranged from 0.32 to 0.47. Both indicated a medium to large effect. These findings suggest that LiDAR Intensity can effectively identify surface fire burn areas even under heterogeneous forest floor conditions. The proposed method has the potential to contribute to enhancing post-fire monitoring using airborne LiDAR.

This study investigates two decade (2000-2020) of Aboveground Biomass dynamics in the biodiversity hotspot of Western Ghats, India, focusing on the impacts on forest fire and climate variability. Using machine learning approaches with GEDI LiDAR data and MODIS satellite imagery, we developed a robust annual AGB model. These analysis reveals a consistent decline in AGB across Kodaikanal and Nilgiris. Results shows that rising temperature and vapor pressure deficit are the key driver for increase in burn are and fire intensity. These are pushing carbon rich evergreen forests toward a critical transition from carbon sink to source. An integrated Structural Equation Model confirms that the dominant role of climate in driving fire regimes and subsequent biomass loss. This research provides a critical scientific foundation for fire adaptive forest management and carbon accounting in vulnerable tropical ecosystem.

Wildfires are longstanding natural phenomena with significant impacts on ecosystems and communities. In recent years, Canada has experienced particularly severe wildfire effects, especially in British Columbia (BC), which has endured prolonged and impactful wildfire events. However, there is currently no specialized wildfire time-series dataset for BC that considers long-term temporal sequences and multiple driving factors, which are essential for data-driven approaches. To facilitate future research on data-driven wildfire risk and spread prediction, we have developed a dataset covering the entire BC province, encompassing 683 wildfire events from 2002-2023 at 500m resolution with daily observations. For each wildfire event, the dataset includes 20 driving factors, including vegetation status, meteorological factors, human activities, topographical features, and active fire detection. Based on this benchmark and similar datasets from other regions, we compared multiple deep learning models, including CNN-based, Transformer-based, and Mamba-based architectures, to explore the effectiveness of existing deep learning models in wildfire risk prediction. We found that model F1 scores were below 0.6, indicating that this new dataset presents a challenging non-linear modeling scenario that requires more advanced and tailor-designed deep learning models to improve wildfire risk prediction accuracy.

Wildfires are known as one of the most important natural hazards, adversely impacting the ecosystems and human lives. Monitoring and management of wildfires is necessary to minimize their negative effects. Global BA products are widely used to study wildfires, but their accuracy is not constant over different environments. In this study, the MCD64A1 BA product was spatially validated using ground truth maps in a fire-prone Zagros Forest over 2021-2023. Our results indicated that its performance varies temporally, as the Kappa coefficient ranged from 0.04 to 0.69. Overall accuracy was higher than 0.96 percent in all years, indicating that MCD64A1 can be considered as a source for studying wildfires; however, its underestimation should be considered. In the next step, the trend of fire and its relationship with precipitation (i.e., obtained from the CHRIPS dataset) were analyzed in three forest ecosystems from 2001 to 2024. In two regions, Marivan and Kermanshah, wildfires experienced an increasing trend, in contrast to the other region, Shiraz, where they decreased over time. Analyzing the correlation between fire and precipitation revealed that spring precipitation is more connected to BA than annual precipitation. Comparing the results of the three regions showed that this matter is also region-related, and the results of one region cannot be referred to another. This study provided information on the performance of MCD64A1 in semiarid forests and the wildfire conditions in the Zagros Mountains to aid wildfire management.

Estimating carbon stock is important for understanding ecosystem dynamics and mitigating climate change. However, biomass mapping in boreal forests faces challenges due to harsh conditions and limited ground truth data for large scale studies. This study presents a parametric model for accurate biomass estimation in the Acadia and Taiga Forest using GEDI Level 4A, Sentinel-2, and SRTM DEM data. We integrated these datasets, and developed the parametric model consisting of spectral bands, vegetation indices, and topographic information with regression techniques, Random Forest and K-nearest neighbour. Results showcase performance of the parametric model with relative weights of variables for accurate Aboveground Biomass Density (AGBD) predictions for the two forest sites. With an average RMSE ranging between 9 Mg/ha to 31 Mg/ha and R^2 values of 0.54 to 0.60, the study reveals the importance of variables like slope, aspect and specific vegetation indices along with raw bands of Sentinel-2 data. Results also demonstrate potential and accuracy limitations of the proposed model with for biomass estimation with high resolution open-source satellite data without ground control. Further research include assessing the model robustness across diverse ecosystems and geographical settings, contributing to sustainable resource management practices.

Accurate estimation of aboveground biomass (AGB) is essential for quantifying forest carbon stocks, monitoring ecosystem change, and supporting greenhouse-gas reporting frameworks. While field measurements remain the benchmark, their limited spatial coverage has driven increasing reliance on remote sensing. Existing global AGB products such as ESA CCI Biomass and GEDI represent major advances but still suffer from signal saturation, sparse sampling, and limited ability to resolve fine-scale structural variation, highlighting an ongoing gap in effectively fusing information from different sensors. Recent studies combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) multispectral imagery have demonstrated improvement in biomass modelling, with machine learning and deep learning achieving R² values from 0.57 to 0.80. However, most work focuses on tropical or broadleaf forests, leaving boreal mixedwood systems underrepresented, and models often rely on short-term composites that overlook multi-year temporal dynamics important for distinguishing long-term growth from seasonal phenology. This study addresses these gaps by developing a Transformer-based deep learning framework that integrates multi-temporal S1 and S2 time series and incorporates Growth & Yield (G&Y) variables as temporal constraints. By leveraging complementary radar–optical interactions and long-range temporal dependencies, the model is designed to reduce signal saturation, enhance structural sensitivity, and improve generalizability across heterogeneous boreal forest conditions.

Fuel Moisture Content (FMC) serves as a vital indicator for monitoring vegetation health and predicting wildfire risk. While existing approaches have largely emphasized temporal variations in FMC, they frequently overlook the inherent spatial clustering patterns of vegetation, leading to compromised spatial prediction accuracy. To overcome this limitation, we introduce a Transformer-based Spatio-Temporal Estimation Framework (TSTEF) that preserves sensitivity to temporal dynamics while incorporating spatial aggregation mechanisms to achieve robust and spatiotemporally consistent FMC estimates. The framework combines spatial autocorrelation analysis with gated recurrent unit (GRU)-based temporal modeling to effectively capture spatiotemporal dependencies, and utilizes Triangular Topology Aggregation Optimization (TTAO) for hyperparameter calibration. The proposed framework was validated using Sentinel-1/2 imagery and MODIS products in California, USA, where it demonstrated: (1) outstanding performance with an average R² > 0.8, MAE < 9%, and relative RMSE of 12.35%; (2) strong agreement between estimated FMC distributions and ground observations, with wildfire burned areas significantly expanding when FMC fell below the 120% critical threshold; and (3) excellent generalization ability during cross-regional validation, achieving relative RMSE values of 20.46% in France, 25.62% in Spain, and 20.76% in Colorado. This study provides a reliable analytical framework for wildfire risk early warning and contributes meaningful insights for ecosystem management amid global environmental change.

In the United States (U.S.), aggressive suppression policies in the 20th century reduced wildfires in the short term, but accumulated fuels contributed to increased wildfire risk in the long term. Land managers are slowly reintroducing the use of fuel treatments, including prescribed (Rx) fires, to remove fuels and mitigate future wildfires. To date, efforts to systematically quantify the efficacy of fuel treatments for wildfire mitigation have been limited in spatial and temporal scope or rely on proxies, such as low-intensity wildfires. Here we use the 30-m Harmonized Landsat and Sentinel-2 (HLS) dataset to analyze timeseries of vegetation “greenness” indices, such as the Normalized Burn Ratio (NBR), with observations up to every 2-3 days. We apply a causal inference approach, difference-in-differences (DID), on HLS-derived timeseries of NBR to compare outcomes in treated and surrounding control areas in three different time phases: 1) post-treatment fuel reduction, 2) wildfire-induced burn severity, and 3) post-wildfire vegetation recovery. As a case study, we targeted 37 Rx fires that preceded and intersected the 2024 Park Fire, a large wildfire in northern California. We show statistical evidence that Rx fires reduce fuel loads (12 Rx fires), wildfire burn severity (12 Rx fires), and post-wildfire vegetation recovery (14 Rx fires). Our approach requires only the spatial footprint and timing of the fuel treatments, thus enabling regional to nationwide analyses using a large number of fuel treatments to quantify the general efficacy of fuel treatments across a variety of treatment types, fuel types, and topography.

Ecosystem extent has been selected as an indicator of biodiversity under the Kunming-Montreal Global Biodiversity Framework. With wildfires on the rise in Canada and around the World there is a need to understand their impact on ecosystem extent for Framework reporting requirements. In Canada’s Arctic and Sub-arctic the growing season is much shorter than at lower latitudes, resulting in few cloud-free optical images and making it a challenge to monitor the impacts of fire and recovery at fine temporal resolutions. Synthetic Aperture Radar (SAR), particularly winter coherence, can be an indicator of ecosystem extent in the sub-arctic because burned areas will exhibit patterns that reflect more dynamic freeze-thaw cycles in the winter period than non-burned areas. Winter coherence and phase was calculated for 39 Sentinel 1 images over the time periods of 2017-2018, 2018-2019, 2019-2020, 2020-2021 in Northern Manitoba, Canada. When relating these values to known fire areas it was found that there is a distinct difference between areas recently burned as opposed to those burned further in the past. These data will be used as predictor variables in a Random Forest ecosystem classifier with outputs of overall accuracy and Shapley feature importance assessed.

Accurate and frequently updated land cover maps are vital for various scientific communities, as well as for public and regional authorities, supporting decision-making, planning, sustainable development, and natural resources management. Regular monitoring and mapping also play a crucial role for agricultural areas, particularly considering the projected population growth and shifting dietary patterns in many of the fastest growing regions of the world, that pose significant challenges for humanity. Over the past decade, the availability of Sentinel-1 and Sentinel-2 data has significantly increased the potential for high spatial resolution land cover mapping using dense time series. However, mapping croplands and distinguishing between crop types remains a more complex task, often requiring data of higher spatial, spectral, and temporal resolution. In this context, this study aims to evaluate the synergistic use of multi-temporal data from Sentinel-1, Sentinel-2, and EnMAP data for detailed land cover and crop type mapping in agriculural regions of western Greece.

Urban localization for autonomous driving requires accurate 6-DoF vehicle pose despite GNSS multipath, occlusions, and rapidly changing visibility. We fuse LiDAR, IMU, and GNSS in an error-state Kalman filter against a high-resolution (HR) map, aiming (i) to reduce LiDAR load without degrading accuracy and (ii) to improve robustness in building-sparse areas such as open junctions. The reference trajectory and HR map stem from a dedicated urban measurement campaign; Monte-Carlo simulations use ray-cast LiDAR, synthesized IMU, and GNSS tied to this trajectory so that only sensor noise is varied. A geometry-aware farthest-point sampling scheme prioritizes points informative for building/ground planes and pole-like structures, while an extended functional model introduces poles as additional vertical constraints. A retained-point rate of 10 % preserves trajectory-wide millimetrelevel and sub-milliradian accuracy, meeting in theory automotive requirements. Filter runtime is reduced by about 82 % relative

to the full LiDAR data. Compared with plane-only variants, the planes+poles configuration yields statistically significant but globally modest improvements in longitudinal, lateral, and yaw accuracy. More importantly, a sliding-window analysis reveals that it markedly stabilizes pose in plane-sparse junctions. Overall, the results suggest that task-aware subsampling preserves trajectory-wide performance while pole constraints add local robustness in challenging urban scenes; validation with real sensor logs remains necessary to confirm these accuracy margins, but the proposed filtering scheme shows promising potential for practical deployment.

Sensor data are increasingly used for monitoring and observation of spatiotemporal phenomena for diverse applications such as in flood management, urban traffic, air quality control, forest fire management, etc. Real time modelling and representation of such evolving phenomena is fundamental for efficient and timeliness decision-making processes. In the context of multisensory systems, where two phenomena (e.g.: air pollution index and windy condition) can both be sensed by networked sensors, analysing the relationship that hold between them is a major issue for decision making. Knowing if the pollution extent is expanding or contracting around a given spot or if it is within a windy zone can help in adopting more appropriate strategies. Sensing system equipped with rule-based reasoning engine to infer on spatiotemporal changes or topological relationship that holds between sensed phenomena with broad boundaries over time will provide decision-maker with adequate and non-ambiguous information. In this paper spatial changes and topological relationship about fuzzy-crisp object modelling the geometry of vague shape phenomena are conceptualized using an Extended Fuzzy Spatiotemporal Change Pattern (FESTCP) and a 5x5 Intersection model (I5x5M) respectively. The rule-based reasoning engine proposed in this paper is based on this conceptualisation. To evaluate our method, a simulated case study of air pollution in Quebec City is carried out. The results reveal that the proposed method captures well the spatiotemporal evolution of a given air pollution episode that served for an on-the-fly decision-making process in real life situations.

Reliable lane-level localization remains difficult for autonomous vehicles (AVs) when Global Navigation Satellite System (GNSS) observations are degraded by blockage, multipath, and non-line-of-sight reception in urban environments. This paper presents PointLoc, an Inertial Navigation System (INS)-centric locator aided by GNSS, monocular visual-inertial odometry (VIO), and High-Definition (HD) Vector Maps. The proposed method is formulated as an INS-centric error-state extended Kalman filter (EKF), in which the INS provides persistent state propagation, while GNSS, VIO, and map matching are incorporated as aiding updates according to their availability and reliability. This design preserves a unified position, velocity, and attitude solution and enables graceful degradation when some aiding sources become unavailable. The method is validated through real-vehicle experiments in Taichung Shuinan and Tainan Shalun under mixed GNSS conditions. The results show that PointLoc achieves the best overall full-route performance in Taichung Shuinan and remains broadly comparable to GNSS/INS/VIO, while still outperforming GNSS/INS, in Tainan Shalun. In the mapped GNSS-denied segment of Taichung Shuinan, PointLoc effectively suppresses vertical drift and substantially improves three-dimensional positioning. The mapped-road analysis further shows that the INS-centric design avoids the planar instability observed in a vision-centric benchmark and provides a more continuous localization solution.

Conventional laser scanning techniques (such as in a Terrestrial Laser Scanner or Mobile mapping), whether used in a static or mobile mode require the object of interest to remain stationary during the scanning stage. Any motion of the object during scanning results in the apparent distortions in the resulting point cloud. The authors in Goel and Lohani (2014b) proposed a motion correction technique to estimate the 3D geometry of a moving object, utilizing a fusion of inertial and GNSS (Global Navigation Satellite Systems) sensors and transformation of the resulting point cloud to an object body coordinate system (OBCS). This paper aims to carry out the experimental validation and performance analysis of the motion correction method. Field experiments are designed and conducted in three phases to verify the correctness of the method. Through this, the paper aims to uncover insights into the performance of the motion correction algorithm and provide the first experimental validation of the proposed technique.

Wearable sensors are essential for gait analysis outside of traditional laboratory environments. However, selection of the right sensor technology involves several trade-offs. Inertial Measurement Units (IMUs) offer high temporal resolution which are ideal for detecting gait events but they suffer from drift. Ultra-Wideband (UWB) provides stable spatial data, but are less precise for detecting event timing. This paper presents a comparative study of three distinct foot-mounted sensor methodologies for heel strike detection and cadence estimation: (1) IMU-Only approach, (2) UWB-Only approach, and (3) a multi-sensor IMU+UWB fusion approach. Each method is evaluated against a camera-based ground truth system using data from four subjects. Results show the IMU-Only method is inconsistent, with moderate event precision (Avg. F1: 0.798), temporal accuracy (Avg. MAE: 47.99 ms), and subject-dependent cadence accuracy (Avg. Acc: 89.59%). The UWB-Only method provides robust event detection (Avg. F1: 0.811) with similar temporal error (Avg. MAE: 49.0 ms) but is exceptionally accurate for cadence estimation (Avg. Acc: 96.94%). The IMU+UWB fusion approach achieves the highest event precision (Avg. Precision: 0.835) and the best temporal accuracy (Avg. MAE: 46.51 ms), while also maintaining robust cadence accuracy (Avg. Acc: 95.62%). In conclusion, while the UWB-Only method is ideal for cadence-only applications, the IMU+UWB fusion approach provides the best overall balance of high event precision, superior temporal accuracy, and reliable cadence estimation.

Accurately registering building footprints from heterogeneous datasets with LiDAR data remains a critical challenge in urban mapping and 3D reconstruction. The objective of this work is to register source data, defined as 2D cadastral vector footprints from structured, regularized, or manually-verified datasets to target building footprints derived from classified aerial LiDAR. LiDAR provides direct 3D information with precise footprint positioning and high spatial resolution, enabling a geometrically reliable representation of dense 3D structures. Conversely, source datasets are not always up-to-date, and may exhibit geometric distortions such as translational offsets, rotational deviations, or local deformations, yet they remain valuable due to their structured organization and metadata content. To enhance geometric fidelity while preserving semantic structure, we propose a practical framework for non-rigid polygon registration that adjusts the geometry of cadastral footprints toward LiDAR-derived targets. The framework consists of two core components: (1) establishing correspondences between source and target polygons, and (2) minimizing a robust distance function that governs the registration process. Three deformation models are introduced: a rigid model allowing translations only, a semi-rigid model allowing deformations while keeping the overall structure of source footprints, and a non-rigid model allowing rotations. We evaluate our method by aligning real cadastral datasets to aerial LiDAR data. The results confirm the effectiveness and robustness of the proposed framework in the context of 2D polygonal cadastral data. This work thus represents the first practical solution for non-rigid polygon registration in this domain.

This paper presents a mask-aware multi-stage depth enhancement framework for digital documentation of historical timber surfaces using RGB–Stereo-IR fusion. Accurate geometric recording of aged wood features such as wooden knots remains challenging due to uneven illumination and weak texture. The proposed pipeline, which aims to stabilise depth geometry under uneven illumination and low-texture surface conditions, integrates object detection, instance segmentation and confidence-guided depth refinement across three stages: (A) TV(total variation)-regularized mask-aware refinement, (B) confidence-weighted multi-view fusion, and (C) patch-based stereo reconstruction. Experiments on historical timber beams under varying illumination demonstrate improved depth completeness and geometric consistency, achieving a residual standard deviation below 0.6 mm in bright scenes and stable reconstruction in low-light conditions. The framework offers a practical solution for depth reconstruction of cultural heritage timber, supporting more reliable feature detection and analysis.

Accurate estimation of Soil Organic Carbon (SOC) is essential for sustainable land management, agricultural productivity, and climate change mitigation. This study presents a novel framework for SOC estimation using machine learning models and diverse predictors, including spectral bands, vegetation and soil indices, topographical features, soil texture components, and HSV-derived soil color proxies. SOC data from 180 samples collected between 2007 and 2020 across 21 fields in Manitoba, Canada, were used for model training and validation. Landsat 5, 7, and 8 data were utilized to extract spectral and soil indices, while SoilGrids and SRTM DEM provided texture and topographical features. Random Forest (RF), Extreme Gradient Boosting (XGB), and a BC-VW-based ensemble model were evaluated across five feature scenarios. The ensemble model achieved the highest accuracy, with an R² of 0.57, RMSE of 0.25, and RMSPE of 7.87%, outperforming individual models. SHAP-based feature selection identified Clay%, SWIR1, and Value (HSV) as the most critical predictors. Independent validation using data from 2021 and 2023 confirmed the model's robustness, with RMSPE values of 10.93% and 12.83%, respectively. This study demonstrates the importance of integrating soil-specific indices, texture, and color features with ensemble modeling to improve SOC predictions. The framework offers a scalable and reliable approach for large-scale SOC monitoring, contributing to sustainable agriculture and carbon sequestration efforts. The findings underscore the need for robust uncertainty analysis and independent validation, setting a benchmark for future SOC modeling studies. 

Soil salinization is a major form of land degradation in drylands, where closed hydrological systems, shallow water tables, and strong evaporative demand favor the recurrent buildup of salts at the surface. Accurate and spatially explicit salinity assessment is crucial for guiding agricultural management and land rehabilitation, yet conventional soil sampling remains spatially restrictive and most remote-sensing approaches insufficiently capture the hydrological and pedological processes that drive seasonal salt redistribution. This study evaluates whether post-rainfall spectral information can improve soil salinity mapping in a large endorheic depression in central Morocco (Sehb El Masjoune). A dataset of 121 ECe-measured topsoil samples was combined with multi-sensor optical imagery from Sentinel-2, Landsat-9, and PlanetScope. In addition to standard salinity, soil, vegetation, and moisture indices, two new post-rainfall predictors were developed: a Depression Proxy (DP), delineating moisture-retentive micro-depressions where salts accumulate, and a Soil Cluster Proxy (SCP), capturing soil textural and compositional contrasts from spectral responses. These predictors were integrated into Random Forest and Gradient Boosting Regressor models and evaluated using repeated cross-validation on Box–Cox-transformed ECe. The combination of DP and SCP with Sentinel-2 predictors yielded the highest performance (R² = 0.92; RMSE = 20.53 dS·m⁻¹), outperforming models relying only on spectral indices and topographic covariates. Seasonal salinity maps revealed strong intra-annual dynamics associated with rainfall events and subsequent evaporative concentration. The proposed DP–SCP framework offers transferable, physically interpretable predictors for dryland salinity assessment and provides a scalable step toward process-informed remote-sensing approaches supporting climate-resilient land-use planning.

Soil total nitrogen (TN) is a critical nutrient for sustainable agricultural management, yet large-scale mapping remains constrained by high laboratory analysis costs. Spaceborne hyperspectral remote sensing offers a promising alternative, but its effectiveness is limited by spectral gaps caused by atmospheric water-vapor absorption in nitrogen-sensitive NIR and SWIR regions. This study evaluates the contribution of reconstructing missing spectral domains to improve soil TN estimation from PRISMA hyperspectral imagery. A spectral gap-filling framework combining a conditional generative adversarial network (cGAN) with a self-supervised masked autoencoder pretraining strategy was developed to reconstruct reflectance spectra across water-vapor absorption intervals (950–990 nm, 1320–1500 nm, and 1780–2050 nm), achieving R² = 0.95 on PRISMA test data and R² = 0.91 against ASD FieldSpec III measurements. Applied to 1,037 samples across three Moroccan agricultural regions, incorporating reconstructed bands consistently improved TN prediction: R² increased from 0.83 to 0.89 in Al Haouz, 0.73 to 0.79 in Doukkala, with R² = 0.73 in Khouribga. Feature-selection analyses identified reconstructed water-vapor bands among the most informative predictors (1050–1450 nm, 1800–2100 nm, and 2300–2400 nm). These findings demonstrate that spectral gap filling enhances spaceborne hyperspectral data usability for operational soil TN monitoring and precision agriculture.

This study investigates the performance of a novel L-band passive microwave radiometer mounted on a Remotely Piloted Aerial System (RPAS) for high-resolution soil moisture retrieval. Soil moisture is a critical variable for predicting crop stress, scheduling field operations, and optimizing irrigation, yet traditional measurement approaches have limitations. Satellite radiometers provide broad spatial coverage but coarse resolution, while in situ sensors offer high accuracy with limited spatial representativeness. RPAS-based sensing offers an intermediate solution, enabling fine-scale mapping with flexible deployment. The sensor evaluated in this research, developed by Skaha Remote Sensing Ltd., measures brightness temperature (Tb) at 1.4 GHz, a frequency where soil emissivity varies strongly with moisture content.

Field campaigns were conducted from May to October 2025 at the Elora Research Station in Ontario, with weekly flights over plots containing different crops and tillage conditions. Concurrent ground measurements of soil moisture, leaf area index (LAI), and vegetation water content (VWC) supported evaluation of vegetation impacts. Statistical analyses, including Pearson correlation and linear regression, revealed the relationships between microwave emissions, soil moisture, and vegetation properties.

Results show a strong inverse relationship between microwave emissions and soil moisture, with vertically polarized signals exhibiting the highest sensitivity. Vegetation effects were crop-dependent due to the unique canopy structures. These findings demonstrate that RPAS-mounted radiometers can provide reliable, high-resolution soil moisture measurements and highlight the importance of crop geometry in interpreting microwave observations.

Drought represents one of the most devastating natural hazards, causing billions in economic losses and threatening global food security. Conventional single-variable drought indices often fail to capture drought's multifaceted nature, while existing composite indices are frequently constrained by linear assumptions or operate as 'black boxes,' obscuring physical drivers. This study introduces the State-Space Gradient Drought Index (SSGDI), developed via a novel Gaussian Mixture Model–Minimum Spanning Tree (GMM–MST) framework that re-conceptualizes drought as a trajectory within a physical system. By modeling a 3D state-space composed of the Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), and Standardized Runoff Index (SRI) with a Gaussian Mixture Model (GMM), the framework learns distinct hydro-climatic archetypes; a Minimum Spanning Tree (MST) then imposes physically plausible connections among these archetypes to define the principal wet-to-dry gradient. The final SSGDI is derived from a data point's probabilistic position along this gradient and is complemented by a classification system that diagnoses the drought's physical type. Applied to the Central China Triangle, the framework successfully uncovered the hydro-climatic system's intrinsic, non-linear structure. Validation showed the SSGDI provides a significantly more robust measure, with SSGDI-6 achieving a spatially-averaged Pearson correlation of r = 0.80 against the PDSI benchmark—a marked improvement over any single component. The SSGDI framework bridges robust statistical aggregation with clear physical interpretation, offering a powerful tool that provides not just a severity score but a diagnostic narrative for proactive drought management.

Synthetic Aperture Radar (SAR) has become a popular form of remotely sensed data for agricultural management due to its ability to acquire cloud-free images at extremely high temporal resolutions. A particularly useful product that can be derived from SAR imagery is coherence, which visualizes structural target changes over time based on phase decorrelation. In a crop management context, coherence is largely unexplored. This is in part due to the fine resolution image requirements that field-scale vegetation monitoring demands. Within agricultural fields, high image coherence should correlate to areas with minimal to no crop growth, whereas low image coherence should correlate to areas where crops are consistently growing. Based upon this hypothesis, our research investigates the applications an ICEYE fine spatial resolution X-band SAR imagery time series has for detecting low yielding regions within corn fields using coherent change detection.

Flooding stands as one of the world's prominent natural hazards, which exerts severe threats to sustainable socioeconomic development. Physical asset information in flood disasters refers to the location, quantity, and damage severity of exposed elements within the affected area. Rapid and accurate extraction of such information is crucial for flood disaster emergency management. To achieve this goal, a remote sensing-based framework for extracting physical asset information in flood disasters is proposed in this paper. This framework summarizes extraction methods for flood damage to typical asset types including cropland, buildings, and roads, and comparatively analyzes the advantages and limitations of multi-source remote sensing data, geographic data, and social media data in physical asset information extraction. This study further investigates the differences between statistical analysis, shallow learning methods, deep learning, and transfer learning approaches, with respect to three key dimensions, namely extraction accuracy, scenario applicability, and computational efficiency. Future research should focus on: (1) Development of operational technologies for flood emergency response and disaster mitigation; (2) multi-source data fusion and dynamic simulation based on digital twin technology; (3) intelligent mining of multi-modal information and development of generalized extraction models driven by foundation models, with the aim of providing technical support for rapid flood emergency response.

Rapid flood damage assessment is essential for emergency response and post-disaster recovery. Following catastrophic flooding in the Haihe River Basin on July 28, 2023, the Dongdian flood detention basin was activated on August 1, with inundation persisting until early October. This study integrates satellite remote sensing, UAV imagery, and field surveys to develop a rapid multi-source approach for comprehensive flood loss assessment. The methodology comprises: (1) extraction of inundation characteristics (spatial extent, depth, duration); (2) classification of exposed assets (agricultural land, forests, residential and industrial areas); (3) comprehensive damage and economic loss evaluation. Results show that 301.49 km² (79.55% of the basin) was inundated from August 1 to October 5, 2023, with an average depth of 2.64 m. The central-western zone sustained the most severe damage, with prolonged residential inundation. Complete corn crop failure occurred, and agricultural-forestry production suffered near-total losses. Direct economic losses exceeded 17.5 billion yuan. Compared to traditional field methods, this approach demonstrates superior efficiency and accuracy, providing scientific support for flood risk management in detention basins.

Coastal environments provide various economic, ecological and societal benefits. Coastal erosion which is the gradual loss of sediment over time, poses a significant threat to South Africa’s coastline. The monitoring and detection of coastal erosion is essential for the effective management of coastal environments. One way to quantify coastal erosion is the delineation of coastal boundaries. Remote sensing techniques such as Synthetic Aperture Radar offers a unique opportunity to extract shoreline positions over large areas of the coast. Furthermore, thresholding and edge detection methods have been successfully used to extract land-water boundaries. In this study, C-band SAR data was used to derive backscatter coefficients for three different areas of interest in the Eastern Cape province in South Africa over a ten year period. The coastal erosion and accretion trends were calculated from the results indicated that the Linear Regression Rate (LRR) for the three different study area showed various coastal erosion seasonality trends. The shoreline LLR ranged between -0.01 and -3.28 m/year for the Cape Recife area and -0.17 and -4.78 m/year for the Nelson Mandela Bay beach front. The overall pattern was erosion during the winter months and accretion during the summer months. In contrast, for the Kings Beach area, there was a consistent accretion trend where the LRR values ranged between 0.94 and 1.68 m/year. The findings confirm that SAR remote sensing is suitable for detecting and monitoring coastal changes in three different coastal environments.

Oil spills in confined maritime corridors often evolve faster than any single satellite mission can observe. This often complicates the interpretation of individual images and create gaps in understanding how a spill progresses between satellite overpasses. This study examines whether combining Sentinel-1 and Sentinel-2 observations can provide a more coherent picture of its development of a spill event, using the case of an oil spill occurred near the Strait of Gibraltar in late August 2022 after a collision between the OS35 and the Adam LNG.

The preliminary analysis evaluated each sensor separately. Sentinel-1 highlighted changes in surface roughness, while Sentinel-2 revealed reflectance anomalies linked to modified optical properties of the water. Since neither dataset on its own offered a complete account of the surface conditions, a fusion procedure was applied to the closest pair of post-event images. The fused map displayed sharper boundaries and more spatial detail than the radar scene alone, offering a clearer outline of the affected area. To address the temporal mismatch between acquisitions, intermediate surfaces were also reconstructed for both sensors, producing estimated representations of the marine conditions at dates not directly observed.

Taken together, the fused and reconstructed products formed a more continuous sequence of the spill’s evolution, capturing both its fragmentation and its short-term reorganisation. Although the approach does not replace dedicated operational monitoring, it demonstrates that combining complementary satellite data can reduce ambiguity in single-sensor interpretation and strengthen situational awareness in regions where surface conditions change quickly and unpredictably.

Windstorms in Peninsular Malaysia have increased in both frequency and severity, posing growing risks to communities, infrastructure, and the national economy. Despite these escalating threats, the region currently lacks a comprehensive, location-specific index capable of evaluating and categorizing windstorm hazards for effective planning and mitigation. This study develops a Windstorm Hazard Index (WHI) tailored to Peninsular Malaysia to assess spatial patterns of windstorm risk and support evidence-based decision-making. Four objectives were addressed: (1) identifying key environmental and geographical factors influencing windstorm occurrences; (2) quantifying these parameters using windstorm records from 2008–2018, numerical simulations generated via WRF-ARW, and urban morphology modelling using Envi-MET; (3) formulating the WHI through the integration of Analytic Hierarchy Process (AHP) and Principal Component Analysis (PCA); and (4) validating the index using documented windstorm events from 2020–2024.The WHI categorizes the peninsula into six hazard levels ranging from very low (0.1–0.5) to extreme (0.901–1.0). Southern and central states, including Negeri Sembilan and Pahang, generally exhibited very low hazard levels, while Kelantan and Terengganu showed moderate risk. High-risk zones were concentrated in northern and coastal regions such as Penang, Kedah, and Perlis, with extreme-risk areas detected in parts of Kedah and Perlis. Results indicate that wind speed, temperature, humidity, precipitation, land use, and urban density strongly influence windstorm intensity, particularly in coastal and densely built environments. Validation confirmed the WHI’s reliability, as extreme-risk classifications aligned with recorded damage patterns. Overall, the WHI serves as a robust framework for regional hazard assessment and disaster-resilient infrastructure development across Peninsular Malaysia.

Flooding is one of the most frequent and costliest hydro-meteorological hazards, impacting every nation and causing significant societal and economic disruption. Despite the abundance of Earth Observation (EO) datasets and hydrodynamic models available to map, monitor, and forecast flood events, decision-makers and first responders often struggle to translate these resources into actionable insights. To bridge this gap, we’ve developed the Flood Risk Index for Resilience (FRI-R), a data-driven machine learning model designed to support resource planning, emergency response, and downstream analytics. FRI-R is powered by the Model of Models (MoM), an operational, open-source ensemble framework that integrates outputs from hydrologic models and EO data from optical imagery. Leveraging historical MoM outputs, FRI-R analyzes the spatial and temporal patterns of past flood events and classifies sub-watersheds from high to low risk based on flood frequency and duration, offering a dynamic lens into vulnerability hotspots. MoM has proven effective in disseminating early flood warnings. Building on this success, FRI-R is designed to enable targeted interventions for at-risk populations and critical infrastructures, thereby empowering communities and decision-makers to proactively mitigate and improve long-term resilience.

This paper introduces the 3D-4CH project and its open framework, i.e. a sustainable ecosystem of tools designed to overcome the fragmentation and limited maintainability of previous EU-funded 3D heritage initiatives. Aligned with the European Collaborative Cloud for Cultural Heritage (ECCCH), the framework integrates an end-to-end pipeline for 3D data generation and processing, semantic enrichment and long-term dissemination, including metadata and paradata inclusion. The 3D-4CH initiative bridges the gap between ICT research and operational heritage practices, ensuring the scalability and reproducibility of 3D digital assets for cross-institutional data sharing and preservation. All software components, including GitHub repositories and online processing frameworks, are openly available, in accordance with open science principles and FAIR data practices. Further information is available at https://www.3d4ch-competencecentre.eu/en/tools/.

The paper presents the integrated 3D metric survey of the Genete Leul Palace in Addis Ababa, demonstrating how metric reliability and operational speditivity can coexist through an adaptive hybrid TLS–MMS workflow that supported the restoration project and heritage documentation in a low-infrastructure context.

Laser scanning, photogrammetry, and other technical tools are staples for cultural heritage documentation and reverse engineering projects. However, manufacturers and even researchers often conflate the data capture process with reverse engineering itself, even though the data alone cannot provide the insight needed for a full reverse engineering or understanding of the historic site. This paper illustrates how laser scanning and photogrammetric applications were used in reverse engineering the construction and details of Conrad’s Jewel, a 1908 Silver/Gold mill in the Yukon, Canada. Analogous to systems and software engineering fields, the reverse engineering process is framed by considering related designs, existing documentation, personal experience, and general external knowledge.

The 3D reconstruction of cultural heritage artefacts plays a crucial role in documentation, conservation and dissemination. While recent advances in photogrammetry, laser scanning and neural rendering techniques have significantly improved the geometric accuracy and visual realism of digitised assets, the reconstruction of transparent and reflective materials - typical in museal collections - remains a major challenge. Materials such as glass, glazes and varnishes exhibit complex optical behaviours, leading to incomplete or inaccurate 3D models. Recent developments in Gaussian Splatting (GS) offer a potential alternative by enabling efficient, high-fidelity scene representation without explicit surface modelling. However, their application to non-Lambertian and transparent heritage objects remains largely unexplored. This paper presents a study on GS methods for the 3D digitisation of transparent cultural heritage artefacts. Through a series of experimental reconstructions, the work investigates the potential and limitations of GS, highlight the opportunities of hybrid pipelines for addressing long-standing challenges in the digitisation of non-collaborative materials.

In recent years, the European Commission (EC) identified the 3D digitization of cultural heritage sites and artifacts as one of its priorities and promoted numerous initiatives and recommendations to accelerate documentation campaigns. However, current digitization targets remain far from being achieved, and heritage institutions have been increasingly encouraged to explore faster and cost-effective 3D documentation solutions. Moreover, traditional image- and range-based 3D surveying techniques frequently struggle when reconstructing objects featuring non-collaborative surfaces (such as reflective or transparent objects), are time-consuming, and require specialized knowledge. Generative AI methods, able to generate 3D models also from a single input image, have recently emerged as a potentially faster alternative, yet their performance on heritage assets remains mostly unexplored. This paper evaluates three state-of-the-art and recent single-image GenAI frameworks - SAM3D, Tripo3D and Trellis2 - on several museum artifacts featuring diffuse, reflective, transparent, and mixed-material surfaces of varying scale and geometric complexity, for which accurate ground truth is available. The aim is to analyze whether these frameworks can act as complementary or alternative solutions for fast heritage documentation.

To address the issues of geometric distortion and loss of details in 3D modeling for complex cultural heritage scenes, this paper proposes an improved 3D Gaussian Splatting (3DGS) reconstruction method that integrates LiDAR and illumination-awareness. First, high-precision 3D coordinates from LiDAR point clouds are utilized to guide the initialization of Gaussian Primitives, establishing a precise geometric foundation and effectively overcoming deformation on weakly textured surfaces. Second, an illumination-aware network is constructed to dynamically adjust appearance parameters by combining global illumination from images with LiDAR reflectance intensity. This decouples complex lighting from material properties, accurately reproducing the unique textures of artifacts. Finally, a multi-dimensional joint loss function incorporating photometric, scale, and appearance smoothness constraints is introduced to collaboratively optimize scene geometry, appearance, and camera poses. Experimental results on indoor and outdoor cultural heritage preservation scenarios demonstrate that the proposed method significantly outperforms various comparative algorithms in terms of both visual fidelity and geometric accuracy. The quantitative and qualitative evaluations confirm that our approach effectively eliminates geometric distortions and recovers fine texture details, providing an efficient and reliable technical solution for the digital preservation of cultural heritage.

Cultural Heritage assets as the Bremen Cog at the German Maritime Museum are often subject to long-term preservation processes and being monitored over time. The Bremen Cog, a clinker-build vessel from 1380, was found in the River Weser in 1962 and thereafter salvaged and reconstructed until 1981. Prior to conservation efforts (1981 to 1999), a photogrammetric 3D measurement campaign was conducted using a stereometric camera SMK 120. Due to deformation a permanent support system was installed in 2003 including the application of local corrections using pressure plates to correct the hull to its measured one from 1980. Since 2020 a long-term geometric monitoring of the cog has been carried out in order to detect deformation. With the analyses of the monitoring data in connection with the measurement conditions, it is of high interest whether the cog in its current shape corresponds to the one estimated in 1980. Historic SMK 120 stereo image pairs on glass plates are analysed in order to estimate their usability and potential for 3D object reconstruction and subsequently comparing the results to the current monitoring data. The proposed workflow includes an optimized digitization process of the glass plate and reconstruction of the interior and exterior orientations. Feature detection and matching methods as well as robust orientation tasks are analysed in order to allow for a 3D hull reconstruction. The reconstruction at least in parts of the cog and with lower precision is desirable and promising in terms of evaluating changes of the hull over time.

Photogrammetry and laser scanning are widespread tools for documenting movable and immovable cultural heritage assets. Documenting the entire surface of an object presents a set of specific challenges, with various solutions currently available. Complete object documentation relies on established capture techniques that utilize the registration method for different model orientations. This paper presents the “Mask Model Method,” a semi-automatic approach for seamlessly documenting entire objects while seeking high-quality results. This workflow works well for most objects that would be considered viable for general photogrammetric capture. The advantages are also in capturing small and large objects (with and without a turntable) with hinge-type moving parts. This method of documenting full architectural artefacts is useful in heritage conservation, repairs, and restoration; specifically, digital patternmaking, virtual reconstruction, digital annotation of historic materials & geometry, and applied experimental archaeology.

We present a residual-aware, multi-sensor 3-D coseismic displacement decomposition applied to the 2025 Mw 7.7 Myanmar earthquake. The workflow combines multi-track Sentinel-1 SAR pixel offsets (range and azimuth) with Sentinel-2 optical pixel offsets, using only the north–south component where the signal clearly exceeds the optical noise level.

The key innovation is to handle sensor- and mosaicking-related residuals within a robust inversion framework rather than as ad hoc preprocessing. Strip-wise and inter-track trends are removed by MAD–Tukey IRLS plane fitting that suppresses long-wavelength orbital and viewing-geometry errors while preserving sharp near-fault steps in overlap zones. A residual-aware weighted least-squares inversion is then performed per pixel to recover east–west, north–south and vertical displacements and their fault-parallel projection. The resulting fields provide spatially continuous, cross-sensor-consistent constraints on fault-parallel slip along this exceptionally long rupture.

In Kolkata, potential land subsidence occurred primarily due to excessive groundwater extraction, which has been one of the major environmental crises, along with rapid urbanization and soft soil characteristics. This study investigates Kolkata's land surface deformation patterns from 2017 to 2023 using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. The study comprehensively analyzes deformation scenarios from 2017 to 2022; additionally, a detailed examination of the 2023 deformation scenario reveals continued trends and localized changes in subsidence patterns. The result shows that the mean ground velocity between 2017 and 2022 varies between -2.8 and -5.5 mm/year, and the area under the subsidence zone shows an increasing trend. Predictive models for 2024 and 2025 are developed based on historical data, providing forecasts of future subsidence trends. The prediction indicates that in 2024, the area under the high deformation class will be relatively higher compared with 2025. Spatial association analyses explore correlations between subsidence patterns of different years in Kolkata. The findings of this study may facilitate the assessment of the possible effects of ground-level movement on resource management, safety, and economics in this densely populated city.

Rapid and accurate detection of landslides after extreme climate events, such as heavy rainfalls or hurricanes, is essential for hazard response and mitigation. Traditional mapping methods rely on manual interpretation or labelled datasets, limiting scalability. This paper presents an integrated workflow combining unsupervised autoencoder-based + KMeans change detection and deep learning semantic segmentation to improve landslide identification in Western North Carolina following Hurricane Helene (September 2024). The approach leverages Planetscope RGB-NIR imagery at 3 m spatial resolution and North Carolina Department of Environmental Quality post-event landslide inventory points. The unsupervised autoencoder extracts latent features and highlights change zones, while segmentation models such as UNet learn spatial–contextual patterns from semi-automated labels. Results demonstrate high detection accuracy with segmentation models achieving strong overlap with ground-truth inventories and minimal false positives with an F1-score of 92%. This hybrid pipeline bridges rapid unsupervised detection and precise pixel-level segmentation, enabling scalable, near-real-time landslide mapping.

Rapid and reliable landslide mapping is critical for post-disaster response, yet Synthetic Aperture Radar (SAR)-based detection remains challenging due to speckle noise, geometric distortions, and complex terrain. This study develops an operational post-event landslide extraction framework using a UNet segmentation architecture with multimodal geospatial data fusion. High-resolution COSMO- SkyMed SAR imagery is combined with terrain representations derived from Digital Elevation Models (DEM), Red Relief Image Maps (RRIM), and rainfall indices to evaluate the contribution of complementary geospatial information to segmentation performance.

Experiments were conducted across three major landslide-triggering events in Japan (Kyushu, Hokkaido, and Kumamoto), comparing SAR-only and multimodal configurations. Results demonstrate that integrating terrain information and rainfall data improves landslide detection performance compared with SAR-only inputs. RRIM consistently outperforms DEM as a topographic descriptor, particularly in steep or heterogeneous terrain, while rainfall information provides moderate gains in recall. Boundary-based metrics further indicate improved geometric fidelity of mapped landslides when multimodal inputs are incorporated. The framework requires only a single post-event SAR acquisition supplemented with publicly available ancillary datasets, enabling rapid and scalable generation of landslide inventories without reliance on pre-disaster imagery. These findings establish a reproducible baseline for SAR-driven landslide segmentation and highlight the potential of multimodal geospatial data fusion for operational disaster response and hazard monitoring.

In this study, we integrated information from the French avalanche database, high-resolution digital elevation models (DEMs), and Sentinel-1 SAR images to model avalanche extents for events occurring across three distinct time periods in three French massifs. The modelled avalanche extents were compared with manually delineated polygons mapped over SAR RGB composites generated using the principle applied in colour-based change detection algorithms. The comparison revealed a strong correspondence between the two independent approaches, with IoU values ranging from 0.42 to 0.47 and F1 scores between 0.58 and 0.63 across the different massifs. We further analyzed the distribution of SAR backscatter values in pre- and post-event images across different zones of the avalanche paths. The results indicated that a fixed 3 dB threshold would most likely be insufficient to capture the complete avalanche extent, as certain zones exhibited backscatter increases of less than 3 dB in post-event SAR imagery. As a result, a multi-threshold approach based on different avalanche zones is recommended. Finally, we assessed the potential of Sentinel-2 optical imagery to detect surface changes and characterize the physical behaviour of avalanche-affected paths following intense avalanche

events. However, the results were inconsistent, exhibiting the expected trends in one study area but nearly opposite patterns in the other, indicating that the integration of optical data for automated avalanche mapping may not always be reliable.

This study presents an integrated remote sensing and deep learning approach for large-scale detection of asbestos-cement roofing in urban environments. Asbestos remains a major environmental health concern across Europe, where asbestos-cement materials persist in the built environment despite regulatory bans. Accurate identification and quantification of these materials are critical for effective remediation planning and equitable health protection.

The research focused on Poland’s two largest metropolitan areas—Warsaw and Kraków—which differ markedly in morphology and historical development, providing contrasting case studies for model validation. High-resolution orthophotomaps (5 cm and 25 cm) from 2023–2024, combined with national building footprint datasets and field-verified information, were used to train and validate a convolutional neural network (CNN) for binary classification of asbestos and non-asbestos roofs.

The highest producer accuracy (90.4%) and overall accuracy (92.9%) were achieved using 128×128-pixel image windows, confirming that broader spatial context enhances classification precision in dense urban settings. The CNN model demonstrated consistent performance across both cities, highlighting its robustness and scalability.

By integrating open orthophotos with open-source analytical frameworks, the method supports the creation of spatially detailed asbestos inventories aligned with the EU INSPIRE Directive and the 2023 Asbestos Directive (EU 2023/2668). The approach enables cost-effective, standardized monitoring applicable to metropolitan and smaller urban contexts alike.

This study advances data-driven environmental health management by demonstrating that deep learning applied to national aerial imagery can deliver operational tools for mapping asbestos exposure risks and informing sustainable, equitable remediation strategies across Europe.

Urban greenspace is essential for ecological resilience, climate regulation, and human well-being, yet long-term, fine-scale assessments of its spatiotemporal dynamics and the extent to which residents benefit from green exposure remain limited. This study develops a 30-m resolution Fractional Vegetation Cover (FVC) dataset to monitor interannual and seasonal variations in urban greenspace across twelve representative Chinese cities from 2000 to 2020. To capture temporal exposure, we introduce the “greendays” metric, defined as the number of days per year that residents experience visible greenery. A population-weighted exposure model was applied to quantify both the magnitude and equality of greenspace exposure. Results show that greenspace increased across all cities over the two decades, with peri-urban areas exhibiting the most substantial gains due to ecological restoration and park development, while core urban areas experienced moderate but consistent improvements linked to renewal and localized greening efforts. Greendays displayed a slight upward trend, indicating an extended duration of annual greenery exposure for residents. Exposure equality remained high and improved in most cities, suggesting that greening initiatives increasingly benefited diverse population groups. Overall, this study provides a robust and scalable remote-sensing-based framework for tracking urban greenspace and exposure equity, offering critical evidence to support nature-based solutions, environmental justice, and sustainable urban planning in alignment with global development goals.

This study develops a generalized method of moments (GMM) framework to quantitatively assess the integrated relationships among climate, vegetation, and lake water volume. Using GSOD precipitation data, SSEBop evapotranspiration, Nino3.4 and MEI indices, and NDVI, we analyzed monthly variations of climatic and vegetation conditions in the Lake Victoria basin from 2000 to 2020. The associations between these factors and lake water-volume changes were first examined, and dynamic GMM was then applied to remove mutual influences among climate variables, allowing for a more reliable attribution of dominant drivers.Results show that precipitation is the primary driver of seasonal to interannual water-volume variations, while evapotranspiration imposes a consistent negative effect on lake storage. ENSO significantly modulates multi-year water anomalies. Vegetation dynamics respond to both climatic variability and lake water-volume changes, with water-level fluctuations providing additional positive feedback after controlling for climate effects.

Posidonia oceanica (Linnaeus) Delile (referred from now on also as P. oceanica) is a marine flowering plant endemic to the Mediterranean Sea, forming extensive underwater meadows that play vital ecological roles, especially as blue carbon reservoirs. Its distribution spans from Gibraltar to Turkey and North Africa to the Adriatic down to 40-50 m of depth (Cocozza et al., 2024). Human impacts, such as pollution, urbanization, and global warming, have reduced its extent by up to 56% in some regions (Robello et al., 2024). Monitoring these meadows is essential, and remote sensing data such as Neo-Pléiades satellite imagery enable their accurate mapping and health assessment. This study applies object-based classification to orthorectified Neo-Pléiades images to evaluate Posidonia oceanica distribution along Sardinia’s eastern coast.

Soil is a crucial component of the ecosystem, affected by climate change, and is often overlooked by remote sensing experts and insufficiently considered while discussing sustainable development projects. To enhance the use of soil related datasets based on earth observation during the planning phase of participatory processes, a specific analysis workflow was piloted during community consultations in Dodoma, Central Tanzania. In order to enhance the integration of the soil conditions during the design of a new community development plan Landsat 8 data from 2023 and 2024 was processed and prepared to make soil information more accessible to non-technical staff and the local communities in Chamwino district. Results confirm the suitability of the SBI as soil indicator thanks to its high resolution, easy interpretability, and context specificity. Preprocessing through experts was identified as viable solution for preparing the data. In addition, field truthing exercises and conversations with the local community members further confirm the accuracy of this dataset for highlighting areas affected by soil salinity or fertility loss and for the final use during participatory planning processes.

This study introduces an automated decision-support tool implemented on Google Earth Engine for mapping vegetated shorelines using Sentinel-2 NDVI. The tool enables reproducible diachronic coastline extraction, rapid processing of large datasets, and supports coastal change monitoring and management applications.

Average rates of shoreline change are key indicators for assessing coastal evolution. The study area is located in Bragança, on the northeast coast of Pará, Brazil, covering urban, estuarine and natural areas. Between 1984 and 2024, despite a general trend of increasing coastline, areas with increasing human occupation experienced significant coastal erosion, causing building retreat, partial loss of homes, and damage to beach access roads. Using the Digital Shoreline Analysis System (DSAS) and time series of dense satellite images processed in Google Earth Engine, the coastline was analyzed in the study area. As a result, the average linear rate of variation showed a slight general retreat of the coastline, accompanied by high morphodynamic variability and low statistical consistency in linear trends. Urbanized sectors exposed to ocean forces were the most vulnerable to erosion, while estuarine and mangrove areas were more stable. The high supply of sediments from the estuaries contributed positively to the addition of the coastline in several regions. These findings emphasize the importance of strategic coastal management considering natural and human influences on shoreline dynamics.

This study proposes a practical pipeline for cross-sensor harmonization and short-term temporal estimation of mangrove leaf reflectance using multi-platform hyperspectral data. We combine laboratory (HySpex VNIR-1800; Days 1/3/7), field (Specim IQ; Day 1), and UAV (Cubert X20 Plus; Day 1) measurements over 400–900 nm for three species (Ceriops tagal, Avicennia marina, A. germinans). Field and UAV spectra are interpolated to the HySpex grid, and multiplicative change factors derived from HySpex Day-1→Day-3/7 trends are used to estimate later-day reflectance for non-lab sensors. Accuracy is assessed via RMSE and Pearson’s r, with focus on chlorophyll-sensitive regions (~450, 680, 720–750 nm). Systematic platform effects appear: in-field spectra exceed HySpex by ~2.5% (A. germinans), ~5.7% (A. marina), and ~11.5% (C. tagal), while HySpex exceeds UAV by ~4.38%, ~7.89%, and ~11.5%, respectively. After harmonization, temporal consistency is strong for A. germinans (RMSE ≈0.047–0.050; r ≈0.958–0.981) and solid for A. marina (Specim RMSE ≈0.066–0.081; r ≈0.943–0.970), with higher UAV variability. Spectral trajectories track post-harvest stress: ~15–20% decline near 680 nm for C. tagal and ~10% for A. germinans, alongside expected green and red-edge/NIR shifts. The workflow enables comparable, temporally resolved spectra across instruments, supporting scalable vegetation phenotyping and long-term mangrove monitoring where single-sensor continuity is limited.

Coastal vegetation provides essential protection against shoreline erosion, wave action, storm surge, and supports biodiversity in low-lying tidal environments. This research discusses methods of using UAS based hyperspectral and multispectral sensors and a deterministic Spectral Information Divergence approach to monitor and preserve the ecosystem in coastal environments. The work focusses on implementing the methodology for monitoring different species of mangrove in a protected natural area located in Florida, USA. The achieved accuracy of 90% proves the ability of UAS based remote sensing system to support a resilience-based restoration and long-term monitoring.

Deforestation remains a critical global challenge with profound implications for food security, ecosystem resilience, and disaster risk reduction. In Sierra Leone, the Western Area Peninsula National Park (WAPNP), one of the country’s last remaining tracts of primary tropical moist forest, faces increasing pressures from illegal logging, mining, and land encroachment despite legal protection since 2012. These activities threaten essential ecosystem services, including water provision, fertile soils, and local climate regulation, while exacerbating vulnerability to floods, landslides, and droughts.

This study evaluates the extent of WAPNP’s closed-canopy forest cover using Sentinel-2 imagery from 2020 to 2024, complemented by very-high-resolution (VHR) data and ground-truth observations for validation. The analysis identifies the main human drivers of forest loss and maps the spatial distribution and remaining extent of forest cover within the park. The results highlight the power of combining Copernicus Sentinel-2 imagery with open-access forest datasets to provide a reproducible, and cost-effective monitoring of forest cover in data-limited tropical regions, offering a valuable tool for conservation planning and management.

The expansion of irrigated crops such as almond and pistachio in arid and semi-arid regions poses a challenge in a context of water resource scarcity. Understanding crop water requirements across large areas has become feasible thanks to remote sensing techniques and the growing availability of satellite imagery with increasingly higher spatial and temporal resolution. However, models have shortcomings that lead to uncertainties in their estimates. In this study, we introduce the model ensemble technique as a method to constrain uncertainty in crop water requirements, with a particular focus on woody crops. This study is centered in the province of Albacete, for the period 2022–2024, and combines two surface energy balance models, METRIC and SenET_TSEB, with a water balance model asssited by NDVI imagery to obtain time series of daily actual crop evapotranspiration (ETa), with a spatial resolution of 20–30 meters. Comparison with in situ measurements recorded at two eddy-covariance towers located in almond and pistachio orchards shows better correlation of the results using the ensemble. At a weekly scale, an average error of 4.9 mm d⁻¹ and 2.8 mm d⁻¹ are obtained for the almond and pistachio crops. Accumulated ETa values over the growing season are consistent and provide confidence to assist in irrigation scheduling, detect stress conditions, and/or quantify water needs at a plot scale. These results reinforce the role of satellite remote sensing in water resources management, in particularly relevant crops for our region such as almond and pistachio orchards.

This study verifies the effectiveness and advantages of spaceborne GNSS-R technology for VWC retrieval, and clarifies that the intercept feature of vegetation observations and Γpeak reflectivity are the core components for constructing high-precision models. The proposed method provides a new technical means for large-scale and efficient VWC monitoring, and has positive significance for improving the assessment of vegetation health and disaster risks.

Beef cattle farming is a significant activity in Brazil, and forage quality has a direct impact on animal performance. However, traditional methods for estimating dry mass, which involve cutting, drying and weighing plant material, are slow and labor-intensive. UAVs equipped with multispectral sensors, such as the DJI Mavic 3M, offer a faster and more scalable alternative for monitoring mixed-forage pastures. This study estimates the dry mass of forage mixtures using multispectral UAV data in two scenarios: (i) using only spectral information and (ii) combining spectral data with canopy height measured in the field. Model performance was evaluated using R², RMSE, and percentage error. The multispectral-only model explained 55% of dry mass variability (720.56 kg/ha; 23.67%), while adding canopy height improved performance to 80% and reduced the error to 589.41 kg/ha (19.36%). Results show that canopy height enhances the accuracy and operational potential of UAV-based methods for estimating dry mass in mixed-forage areas.

Mining companies are continuously looking for cost efficient methods to monitor the success of their rehabilitation efforts. Although open access satellite imagery is available at regular temporal intervals, its usefulness for grassland biodiversity monitoring has been questioned due to its coarse spatial resolution with respect to the species size. To compensate for the low spatial resolution, previous studies have successfully explored the benefits of using a multitemporal set of Sentinel-2 (S2) images. However, unless the temporal patterns are studied as a whole, some of the phenological information such as growth rates are lost, and delayed snow cover may spread events like growth onset over multiple dates between plots. This study aims to explore the added value of temporal fitting of Sentinel-2 time series (ts) over existing baseline models applied using the full time series as such. Our set of temporal features included functional components, harmonic decomposition, frequency decomposition, and phenological metrics. Out of the compared models, the Random Forest regression model using a set of fitted temporal features achieved the highest holdout prediction accuracy (R2 = 0.36, RMSE = 3.87, relative RMSE = 0.20) and cross-validation accuracy similar to the baseline models. However, all the compared regression models underestimated extreme plant diversity to some extent. Future studies should account for varying vegetation cover and terrain features by incorporating auxiliary data.

Woody plant encroachment (WPE) threatens grassland ecosystems across the Canadian Prairies, causing grassland biodiversity loss with substantial economic impacts due to reduced forage production. While remote sensing offers scalable monitoring capabilities, existing approaches lack frameworks for distinguishing shrub and tree encroachment and often require extensive ground truth data. This study developed an ensemble machine learning framework integrating Sentinel-1 SAR and Sentinel-2 optical imagery with UAV-derived training data to map fractional shrub and tree cover across Saskatchewan's Aspen Parkland and Moist Mixed Grassland ecoregions, SK. A stacking ensemble combining Random Forest, Support Vector Machine, XGBoost, and Artificial Neural Network models with Ridge regression meta-learning outperformed individual algorithms, achieving mean R² values of 0.65 for shrub and 0.68 for tree cover prediction. Multi-scale training incorporating features at 10, 30, 50, and 70 m resolution improved performance by 15% for shrub and 24% for tree compared to single-scale approaches. Feature importance analysis revealed that shrub detection relied primarily on red-edge bands and moisture indices, while tree detection depended heavily on SAR backscatters. Quantile histogram matching enabled successful model transfer from Foam Lake Community pasture to Aberdeen Community Pasture, with resulting maps indicating that total WPE exceeded 50% in both study areas, with shrubs occupying 23.7% (Foam Lake) and 18.5% (Aberdeen) of both regions at rates higher than 5% shrub cover. The present framework provides a scalable, cost-effective approach for operational woody encroachment monitoring, enabling early detection and targeted functional management interventions to preserve grassland ecosystems.

Fractional vegetation cover (FVC) is a key land surface parameter describing vegetation abundance and structure, defined as the fraction of the ground area occupied by vegetation when viewed from nadir. FVC provides essential insights into ecosystem condition, productivity, and disturbance, making it a critical variable for biodiversity monitoring and habitat assessment. However, generating accurate and repeatable FVC estimates remains challenging due to scale effects, spatial resolution constraints, and inconsistencies in available validation data across time and space. This research develops a machine learning (ML) framework for large-scale FVC estimation that addresses these challenges by combining multi-sensor Earth observation data and Active Learning (AL) model refinement techniques. The ML framework is applied within key wood bison habitat in northern Alberta, focusing on mapping six vegetation components: spruce, pine, deciduous, shrub, herbaceous, and moss. The approach integrates Sentinel-1, Sentinel-2, Landsat-9, and GLO-30 data, optimized through feature selection and ensemble-based Random Forest modeling. The resulting FVC maps achieved strong predictive performance (R² = 0.50–0.88) and capture fine-scale spatial variability in vegetation composition. The ML pipeline provides a scalable and adaptive framework for FVC estimation that supports provincial landcover updates, improves understanding of wood bison habitat features, and contributes to ongoing ecosystem monitoring and conservation planning across boreal Alberta.

Wildlife monitoring from aerial imagery often requires precise animal localization under practical constraints where only object counts are needed. Traditional detection methods rely on bounding-box annotations, introducing unnecessary cognitive load for small objects spanning only a few dozen pixels. This work introduces DINOKey, a modified DINO transformer-based detector adapted to operate natively on point annotations rather than bounding boxes. Key contributions include: (1) architectural modifications to the DINO decoder, detection head, and denoising queries to directly predict 2D keypoints; (2) a combined loss function integrating L1 regression, focal loss, and average Hausdorff distance, with ablations validating each component; (3) open-source implementation within an existing detection framework; and (4) demonstration of improved small-object localization and reduced false positives on an aerial elephant dataset compared to box-supervised baselines. Ablation studies show that the Hausdorff distance term provides the largest accuracy gain by effectively reducing false positives, while focal loss improves stability in densely clustered regions. The proposed method achieves 0.786 mAP and accurately localizes animal centers across diverse environmental conditions, offering a practical solution for conservation practitioners working under tight logistical constraints.

Fog-dependent ecosystems in the Atacama Desert host highly specialized vegetation, yet monitoring their functional traits remains challenging due to remoteness and limited spectral detectability. The bromeliad Tillandsia landbeckii exhibits extremely low reflectance in the VIS/NIR range, rendering conventional multispectral approaches ineffective.

This study evaluates the potential of a novel UAV-based VNIR/SWIR multi-camera system (camSWIR) for estimating canopy water content (CWC) in Tillandsia landbeckii. A UAV survey conducted in northern Chile acquired high-resolution (≈3 cm GSD) SWIR imagery across four operational bands (1100–1650 nm). Field-based destructive sampling (n = 20) provided reference CWC measurements, and a statistically rigorous workflow was applied to mitigate overfitting in a high-dimensional predictor space.

Results show that the spectral slope between 1200 and 1510 nm is the most informative predictor of CWC, with cross-validated performance indicating moderate predictive skill (LOOCV R² ≈ 0.52), but reduced stability under nested validation. The repeated selection of predictors within this wavelength region confirms a physically meaningful relationship with liquid water absorption.

Despite limitations due to a small sample size and species-specific optical properties, particularly the dense trichome layer that affects light interactions, the study demonstrates the feasibility of SWIR-based, non-destructive CWC estimation in hyper-arid ecosystems. These findings provide a proof of concept for future upscaling, highlighting the need for larger calibration datasets and improved modelling to enable reliable spatial mapping of plant water status.

Aerial imagery provides a powerful avenue for monitoring wildlife populations, yet automated detection remains challenging. Animals typically occupy only a tiny fraction of large-scale aerial imagery, may be partially obscured, and appear against highly diverse Arctic and sub-Arctic backgrounds. Suppervised deep-learning detectors also depend on large, fully annotated datasets, making broad ecological surveys labor-intensive and slow to scale.

This study explores an alternative perspective: viewing wildlife as rare events within mostly background imagery. Instead of training on annotated animal samples, an anomaly-detection framework learns the visual patterns of normal landscapes and identifies deviations from these patterns as potential animal locations. To guide the model without costly labels, simple animal-like shapes are inserted into background patches during training, encouraging the network to recognise features associated with real targets while avoiding the need for detailed masks or bounding boxes.

The approach generates two outputs: patch-level predictions distinguishing empty from potentially occupied areas, and pixel-level anomaly maps highlighting likely target locations. When evaluated on a highly varied Arctic dataset, the method remains reliable despite major shifts in terrain, surface texture, animal distributions and postures, and pronounced class imbalance that often degrade supervised models. Unlike distribution-based anomaly approaches that rely on stable normal-feature statistics and frequently misinterpret natural texture variability as anomalies, this method handles heterogeneous environments more effectively.

Overall, the study shows that anomaly-oriented frameworks, typically used in industrial and medical settings, have strong potential to ease annotation demands and support scalable, automated wildlife detection in complex remote-sensing environments.

Rapid and reliable assessment of building damage after major earthquakes is essential for effective emergency response and recovery planning. This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies. The analysis focuses on the Mw 7.7 Myanmar earthquake of 28 March 2025 and integrates post-event COSMO-SkyMed Second Generation (CSG) dual-polarization (HH, HV) SAR imagery, Maxar optical data, OpenStreetMap (OSM) building footprints, and UNOSAT damage annotations. Three fusion paradigms are evaluated: Early Fusion (EF), Late Fusion (LF), and a novel Middle Fusion (MF) approach. The proposed MF framework introduces a Footprint-Guided Cross-Attention (FGCA) mechanism that uses building geometry as a spatial prior to guide feature-level interaction between SAR and optical representations. Five-fold cross-validation results show that MF consistently outperforms EF and LF, achieving higher precision, F1-score, and robustness across modality configurations. By jointly exploiting SAR structural sensitivity, optical detail, and footprint-based spatial context, the proposed Footprint-Guided Middle Fusion (FGMF) framework enables accurate and scalable building damage mapping from heterogeneous Earth Observation (EO) data.

Accurate and timely building damage detection (BDD) is crucial for disaster emergency response. Although deep learning-based change detection methods have made significant progress in remote sensing, their practical application in disasters still faces two major challenges: (1) Existing high‑accuracy models are typically computationally complex and difficult to deploy for real‑time inference on edge devices.. (2) Model performance heavily relies on large amounts of annotated data, but disaster data are extremely scarce. To address these challenges, this paper proposes a novel lightweight Local‑Global Interaction Network (LGINet) for efficient BDD. The core of LGINet is the proposed Local‑Global Interaction Unit (LGIU), which achieves efficient fusion of detailed and contextual features through a dual‑path architecture and channel‑wise cross‑attention mechanism. Furthermore, a Frequency Difference Enhancement Unit (FDEU) is proposed to generate more accurate damage features, and contrastive learning is employed to reduce the model’s sensitivity to weather conditions and its reliance on annotated data. Experimental results on the xBD and WBD datasets show that LGINet achieves F1-scores of 81.76% and 80.91%, respectively, with an inference speed of 47.83 FPS. It achieves the best balance between accuracy and efficiency, outperforming existing methods.

Recent armed conflicts have increased the need for reliable, spatially explicit damage mapping to support situational awareness, humanitarian assessment, and reconstruction planning. This contribution presents a hybrid change-detection framework for conflict-related urban damage mapping that combines AlphaEarth Foundations embedding change with Sentinel-1 SAR change indices. AlphaEarth provides semantically informed annual embeddings, while Sentinel-1 time series contribute all-weather sensitivity to structural change.

The study compares several embedding-based change metrics and combines the selected AlphaEarth indicator with SAR-derived change measures through simple scalar fusion rules. The proposed framework is designed to preserve the sharp sensitivity of SAR to abrupt structural changes while reducing part of the diffuse background response that often complicates single-source interpretation.

Experiments are conducted over war-affected urban areas in Ukraine, with illustrative examples from Bakhmut and Avdiivka. The results show that AlphaEarth and Sentinel-1 provide complementary information and that their fusion improves the spatial specificity of detected damage patterns. The contribution highlights the potential of combining foundation-model representations with radar time series for operational damage mapping in conflict settings.

Rapid and precise identification of disaster locations is essential for efficient emergency response and management. However, during the immediate post-disaster phase, the lack of timely and reliable information often impedes relief operations. Although satellite imagery and ground-based sensing systems provide valuable data, their effectiveness is constrained by factors such as time delays, high costs, and limited spatial resolution. At the same time, social media platforms such as X (formerly Twitter), Instagram, and Facebook have become valuable channels for real-time, crowd-sourced information. Users function as "human sensors," contributing extensive on-the-ground insights. Much of this content is visual—images that capture the effects of disasters with finer street-level detail and immediacy than textual posts. In this study, we propose a novel, deep learning-based image-matching framework designed to pinpoint the geographic coordinates of disaster events from social media images with street-level accuracy. The core of our approach is to match a query disaster image against a database of georeferenced Google Street View (GSV) imagery. The methodology consists of image pre-processing and feature enhancement; deep feature extraction and matching, and location inference and verification. The preliminary results on an external validation dataset are highly promising, demonstrating a high detection rate of ~90% with confidence scores above 0.9. The model proves resilient to key challenges such as partial occlusion and varied lighting, accurately segmenting multiple objects against complex backgrounds of damaged structures and flooded areas.

The paper presents a novel deep learning framework for automated disaster damage assessment using remote sensing imagery. It addresses the challenge of timely and accurate damage classification in the aftermath of disasters, aiming to improve emergency response and resource allocation. The proposed system leverages both pre- and post-disaster satellite images to assess building damage across four categories: no damage, minor damage, major damage, and destroyed. The central innovation lies in the development of a multi-modal attention mechanism, which integrates features from both pre- and post-event images to enhance damage detection. A lightweight ConvNeXT-Tiny architecture serves as the backbone, ensuring efficient processing while maintaining high performance.

Key contributions of this work include: (1) a cross-attention module that fuses multi-modal data, (2) an optimized preprocessing pipeline designed for large-scale datasets, and (3) novel data augmentation techniques that improve the model’s robustness. Experiments on a large-scale disaster damage dataset show the model achieves an impressive 94.90% classification accuracy, with strong performance in discriminating damage levels and resilience to incomplete or corrupted data. This framework represents a significant step forward in disaster response, offering a scalable solution for real-time damage detection. The research demonstrates the potential of combining remote sensing, multi-temporal imagery, and deep learning to expedite and improve disaster damage assessment, ultimately supporting more efficient emergency management.

Urban areas evolve rapidly, often increasing exposure to natural hazards, especially in seismically active regions such as southern Turkey. This contribution presents an AI-based workflow for multi-temporal analysis of urban expansion in the city of Osmaniye between 2015 and 2025. The methodology integrates Sentinel-2 multispectral imagery with a U-Net convolutional neural network trained on World Settlement Footprint (WSF) masks for binary segmentation of built-up versus non-built-up areas. After training on 2015 and 2019 data, the model was applied to the full temporal series to assess its generalisation capability and to quantify long-term urban growth.

Results show a substantial increase in built-up surfaces over the decade, with a temporary decline linked to the 2023 earthquake and a marked acceleration during the reconstruction phase. Beyond the quantitative trends, the spatial patterns identified by the model highlight how urban expansion has progressively shifted from the central districts toward peripheral zones, revealing both densification processes and outward sprawl. These observations provide valuable indications on how development pressures interact with seismic vulnerability.

The approach demonstrates the potential of AI and open satellite data for large-scale, reproducible monitoring of urban dynamics and for supporting risk-informed urban planning. Because it relies entirely on open-source datasets and tools, the workflow can be easily transferred to other hazard-prone regions, offering a scalable and transparent framework for assessing urban change, post-disaster reconstruction, and long-term exposure.

Urban surface structure and component temperatures induce significant thermal anisotropy (TA), resulting in substantial differences in observed surface temperatures across varying viewing angles. Although previous studies have investigated the temporal dynamics of TA through observations and modeling, its spatial differentiation over heterogeneous surfaces remains poorly constrained. Resolving how surface heterogeneity influences TA is hindered by the coarse spatial resolution and limited angular sampling of current multi-angle satellite observations. Consequently, most mainstream thermal-anisotropy models were developed for simplified scenes and lack systematic evaluation of their applicability to complex urban environments. To address these challenges, we coupled the microscale 3D urban energy balance model (TUF-3D) with the state-of-the-art Discrete Anisotropic Radiative Transfer (DART) model. This approach allows for rapid and accurate TA modeling of hypothetical urban scenes with varying geometric structures and component temperatures, thereby quantifying the impact of surface heterogeneity on TA. Building height variability was used to represent geometric heterogeneity, while differences in building material properties were used to characterize component temperature heterogeneity. To evaluate , The results of a series of sensitivity experiments have validated the individual effects of geometric and component temperature heterogeneity on TA. From the perspective of component temperature, changes in average component temperatures result in a maximum TA difference of 7.29 K, while temperature variability alone contributes only 0.54 K. These findings suggest that assuming simplified scenes with uniform building heights or homogeneous component temperatures can introduce biases in TA simulations, potentially compromising the accuracy of models correcting for the angular effects of land surface temperature.

Understanding the thermal behavior of soil and vegetation separately is essential for interpreting land–atmosphere energy exchange, diagnosing ecosystem stress, and improving land surface modelling. However, conventional satellite LST products only provide a mixed radiometric signal, masking the distinct thermal responses of soil and canopy. This study introduces GloSVeT, the first global dataset that provides monthly surface soil and vegetation component temperatures at 0.05° resolution for 2003–2023. The dataset is generated using an enhanced multisource fusion framework that integrates multi-temporal MODIS observations with ERA5-Land skin temperature and vegetation structural information to retrieve physically consistent component temperatures. We summarize the data sources, modelling framework, and global implementation strategy, and present an independent evaluation using flux-tower networks with screened spatial representativeness. Validation results show strong agreement with in-situ measurements, with correlations typically above 0.9 and RMSE around 2 K for both soil and vegetation temperatures. Seasonal variations in performance reflect expected hydrothermal conditions, and a small cool bias is attributable to the temporal sampling of satellite observations. GloSVeT provides a new basis for studying surface energy partitioning, monitoring hydrothermal dynamics, and supporting ecosystem and climate model applications.

This study presents the development of a ground-based observation system designed for vicarious calibration of satellite sensors operating in the mid-wave infrared (MWIR) region. Conventional natural targets used in LWIR calibration lack spectrally stable emissivity in MWIR, motivating the need for dedicated reference targets and high-sensitivity measurement instruments. We introduce a thermally controlled ground reference target whose effective emissivity can be tuned by adjusting the ratio of water and metal surfaces using perforated plates of varying hole diameters. In parallel, an MWIR radiation thermometer employing lock-in detection was developed to enable accurate measurement of low-signal MWIR radiance from room-temperature targets. The system achieved measurement uncertainties down to 20–70 mK. A field campaign was conducted at the Goheung Aerospace Center using the integrated reference targets and radiation thermometer to validate performance under real environmental conditions. The results demonstrate the feasibility of applying controlled emissivity targets and lock-in-based MWIR radiometry to improve the accuracy of MWIR vicarious calibration frameworks.

A Method for Identifying Industrial Heat Sources

This study addresses the limitations of conventional urban shortwave radiation simulations in representing complex three-dimensional morphology. A parameterization approach for large-scale urban sky view factor was proposed, significantly improving computational efficiency and spatial adaptability. Based on this, an urban solar shortwave radiation transfer model was developed to quantitatively characterize the shading and reflection effects of building clusters. Furthermore, a novel remote sensing inversion method for urban surface reflectance and solar radiation parameters was introduced, enabling high-accuracy estimation of surface radiative properties and offering a new technical pathway for urban thermal environment and energy balance research.

Urban Heat Islands (UHIs) emerge from reduced vegetation, impervious surfaces, and anthropogenic heat emissions, leading to elevated surface temperatures in urban areas. Monitoring UHIs at fine spatial and temporal scales requires thermal data capable of capturing both urban heterogeneity and daily variability—conditions not satisfied by the native 1 km resolution of MODIS Land Surface Temperature (LST).

This study presents a regime-aware machine learning workflow to downscale daily MODIS LST to the native spatial scale of MODIS NDVI (231 m) over Bologna (Italy), using only MODIS-internal predictors and meteorological forcing. The approach adopts a two-stage architecture: a Ridge regression model estimates a day-level atmospheric bias, while a Random Forest reconstructs pixel-level residuals to recover fine-scale thermal variability from vegetation, land-cover, topographic, and atmospheric predictors. To account for atmospheric control, the dataset is partitioned into three thermal regimes (COLD, MILD, HOT), with independent models trained for each regime.

Pre-processing and data integration were performed in Google Earth Engine using MODIS LST (MOD11A1/MYD11A1), NDVI, SRTM-derived terrain variables, and built-up fraction from ESA WorldCover. Experiments show strong predictive performance (RMSE < 1 K; R² ≈ 0.90) and spatial patterns consistent with Local Climate Zones.

The MILD and HOT regimes provide the largest enhancement in spatial detail compared to the original MODIS product, while the COLD regime shows reduced performance, likely due to weaker surface–atmosphere coupling. Results highlight that atmospheric conditions play a dominant role in downscaling accuracy, exceeding the impact of model architecture. The framework enables scalable, daily UHI monitoring and supports heatwave analysis and climate-resilient urban planning.

Nighttime optical remote sensing provides valuable insights into natural and, in particular, human activities. This study evaluates the nighttime imaging capabilities of the Sentinel-2 mission using the only available nighttime acquisition not limited to ocean observations for dark signal calibration, covering the United Arab Emirates with Dubai in 2015. We checked the detection limit using granules over the Persian Gulf, extracted radiance spectra for different regions of interest, and analysed lighting types and temperatures. Results suggest a conservative nighttime detection limit of approx. 0.37 W/m²/um/sr for visible/near infrared bands, and 0.08 W/m²/um/sr for short-wave infrared bands. Sentinel-2’s high spatial resolution and multispectral bands, although designed for daytime observations, were capable of detecting and classifying bright visible/near and short-wave infrared emitters. Comparisons with hyperspectral EnMAP imagery acquired in 2025 validated the classifications and revealed changes in urban lighting over a decade. While limitations apply, this study highlights S2’s potential for nighttime remote sensing and supports considerations of nighttime capabilities for future satellite missions.

Climate change is intensifying droughts, creating an urgent need to understand these events and take necessary mitigation actions. This work focuses on the Bundelkhand region of Uttar Pradesh, India, an area that frequently experiences severe water stress and is highly susceptible to drought. We used multi-source remote sensing datasets to monitor drought conditions through established drought indices. The analysis period spans from 2000 to 2021. CHIRPS data were used to calculate SPI and RAI, while PKU GIMMS NDVI data were used to calculate VCI. ERA-5 Land was used for soil moisture data to derive SMCI. To track how drought propagates, we performed a correlation analysis between the indices representing meteorological and agricultural drought. The results show that the datasets and the methods are suitable for identifying droughts in the region. Historical drought episodes were accurately detected, and the analysis of the 2015 drought revealed its onset from June to September, which aligns with the monsoon season in Bundelkhand. The datasets and indices used provide a practical and reliable output for sparse ground-based observations for regional drought monitoring and management.

The Near-global Combined Drought Monitoring (NEC-DROMO) dataset provides a comprehensive depiction of drought conditions by integrating multiple agro-climatological variables across global land areas. Spanning 2002–2021 at a monthly time step and 0.25° spatial resolution, NEC-DROMO combines soil moisture, vegetation water content (VWC), rainfall, and temperature to capture both agricultural and meteorological drought signals. A key feature of the dataset is the use of Principal Component Analysis (PCA) to derive dynamic, month-specific weights for each variable, allowing the Combined Drought Indicator (CDI) to reflect seasonal and regional variability in drought drivers.

The dataset is built primarily on inputs from the ECoHydrological Land Reanalysis (ECHLA), which provides soil moisture, VWC, and temperature derived from passive microwave observations assimilated through a land–vegetation model. Rainfall fields are obtained from the ERA5 reanalysis, ensuring consistency across atmospheric and land-surface conditions. Validation against satellite-based indicators, ground observations, and event-based disaster datasets demonstrates NEC-DROMO’s strong capability to reproduce observed drought patterns globally.

With its multi-variable foundation and long-term coverage, NEC-DROMO serves as a valuable resource for drought monitoring, climate analysis, food-security assessment, and agricultural risk management. It supports detailed historical analyses and offers an integrated perspective for users seeking reliable, spatially consistent drought information.

Tropical terrestrial vegetation is critical to the global carbon cycle but faces escalating drought threats. Traditional assessments using fixed climate thresholds often ignore actual physiological responses and non-moisture disturbances. To address this, we developed a novel framework that isolates the true physiological impacts of atmospheric and soil moisture (SM) deficits to identify growing-season vegetation droughts (1982–2019). Results reveal pantropical increases in drought intensity, with tropical forests experiencing significantly sharper intensifications than other biomes. Regionally, African forests exhibit the most severe expansions in drought intensity and area. Interpretable machine learning attributes this intensifying drought predominantly to declining SM (NDVI: 52.1%; LAI: 53%). Finally, while reliable historical reconstruction is vital for future projections, CMIP6 models fail to reproduce these observed trends. These findings highlight mounting drought pressures on tropical forests and underscore the critical need for improved climate models to inform mitigation strategies.

Extreme heat events (EHEs) pose growing risks to densely populated subtropical cities such as Hong Kong, yet there remains a need for lightweight, interpretable tools that can provide multi-day forecasts based on readily available observations. This study develops a multi-source data driven framework that integrates aerosol optical depth (AOD), land surface temperature (LST), precipitable water (PW), and precipitation (Precip), together with ARIMA-based anomaly features, to predict EHEs over Hong Kong. Using a seven-day sliding window, independent XGBoost classifiers are trained to forecast daily EHE occurrence probabilities for the next 1–5 days over ten climate years (March 2015–February 2025). A lead-specific threshold optimization on a validation subset is applied to maximize F1-score. Test results show that AUC values for Lead 1–Lead 5 remain between 0.935 and 0.883, with F1-scores between 0.738 and 0.639, indicating robust predictability up to five days in advance. A process-scale duration inference method based on the leading continuous segment of the predicted sequence achieves 67.08% exact-match accuracy, 77.69% accuracy within ±1 day, and a mean absolute error of 0.75 days. The proposed framework is computationally efficient and operationally relevant, offering practical support for urban heat early warning and risk management.

Anthropogenic climate change and land use transformations are interactively reshaping environmental risks. This study investigates the critical feedback between Land Use/Land Cover (LULC) change and shifts in Köppen-Geiger (KG) climate zones in Taiwan from 2001–2020, and their combined impact on disaster hotspots.

Using MODIS and CHIRPS data alongside a comprehensive disaster inventory, we quantified the spatial co-occurrence of LULC change and climate zone transitions. Our preliminary results reveal a significant climatic shift, with over 10,500 km² transitioning from tropical monsoon (Am) to a drier tropical savanna (Aw) climate, alongside substantial wetland loss and urban expansion. We hypothesize that these dynamic "climate transition zones" are emerging fronts of heightened disaster risk.

Our analysis tests whether areas undergoing active climate reclassification concentrate a disproportionate share of historical landslides and floods. The expected outcome is a novel, dynamic risk assessment framework that moves beyond static models. By identifying these emerging hotspots, this research provides a critical tool for proactive land-use planning and climate-resilient disaster risk reduction, with methodologies applicable to other complex, hazard-prone regions.

Climate change is amplifying the occurrence and intensity of Extreme Event Induced Disasters (EEID), such as floods and wildfires, which increasingly threaten societies and ecosystems. Fast and accurate monitoring tools are therefore essential for damage assessment and emergency response. Remotely sensed data, particularly from the Copernicus Sentinel-2 mission, provide valuable multispectral information for large-scale environmental monitoring, but their manual analysis remains time-consuming.

Recent advances in Deep Learning (DL) have enhanced classification, segmentation, and change detection of geospatial data. New multimodal Prompt-Based (PB) architectures integrate image and text inputs via Text Encoders (TEs), enabling zero-shot detection of previously unseen objects. These models promise flexible, prompt-driven analysis but often underperform compared to Object-Specific (OS) models optimized for particular tasks.

In Earth Observation (EO), foundation models such as Prithvi-EO and TerraFM mark a major step forward, offering generalized pre-training across vast multi-sensor datasets to support downstream OS tasks with limited data.

While DL traditionally requires coding expertise, commercial GIS platforms now integrate DL tools accessible through Graphical User Interfaces (GUIs), allowing inference and limited fine-tuning of pre-trained models. This democratizes DL access for GIS users but shifts expertise toward model evaluation and interpretability.

This study systematically compares PB and OS models executed through both GUI-based and Python environments using Sentinel-2 flood and wildfire imagery, assessing accuracy, flexibility, and processing efficiency to evaluate the balance between accessibility and performance in the democratization of DL for EEID monitoring.

Multi-modal Satellite Image Time Series (SITS) analysis faces significant computational challenges for live land monitoring applications. While Transformer architectures excel at capturing temporal dependencies and fusing multi-modal data, their quadratic computational complexity and the need to reprocess entire sequences for each new acquisition limit their deployment for regular, large-area monitoring. This paper studies various dual-form attention mechanisms for efficient multi-modal SITS analysis, that enable parallel training while supporting recurrent inference for incremental processing.

We compare linear attention and retention mechanisms within a multi-modal spectro-temporal encoder. To address SITS-specific challenges of temporal irregularity and unalignment, we develop temporal adaptations of dual-form mechanisms that compute token distances based on actual acquisition dates rather than sequence indices.

Our approach is evaluated on two tasks using Sentinel-1 and Sentinel-2 data: multi-modal SITS forecasting as a proxy task, and real-world solar panel construction monitoring. Experimental results demonstrate that dual-form mechanisms achieve performance comparable to standard Transformers while enabling efficient recurrent inference. The multi-modal framework consistently outperforms mono-modal approaches across both tasks, demonstrating the effectiveness of dual mechanisms for sensor fusion. The results presented in this work open new opportunities for operational land monitoring systems requiring regular updates over large geographic areas.

Sensor harmonization is required to produce consistent Landsat imagery for long-term change detection. This study investigated the effect of seasonality and aerosol optical thickness (AOT) on linear harmonization functions, which are frequently used to create consistent Landsat 7 ETM+ and Landsat 8 OLI time series data. We found that training harmonization functions with pixels that have low or average AOT can greatly reduce the difference between near-coincidental Landsat 7 and Landsat 8 observations, and that seasonally trained harmonization models outperform models trained on year-round data. We assessed the effect of ETM+/OLI sensor harmonization on forest type classification with a Random Forest model, and found that seasonally harmonized imagery provided more consistent classification maps than the alternatives. This study illustrates important details related to the creation of harmonized datasets and is a significant step toward creating more consistent Landsat 7 and Landsat 8 data for long-term change detection analysis.

Urban growth in Ecuador's inter-Andean valleys has accelerated the territory's transformation, driven by the expansion of road infrastructure and the occupation of environmentally fragile areas. In this context, the Ruta Viva highway has reconfigured urbanisation patterns in the parishes of Cumbaya and Tumbaco, advancing the urban frontier into agricultural areas and moderate slopes. The objective of this study is to evaluate the dynamics of urban expansion in the parishes of Cumbaya and Tumbaco during the period 2002-2032, using a multitemporal and predictive approach to project future urbanisation scenarios and generate inputs for sustainable territorial planning and land management. The methodology integrated multitemporal analysis of land use and land cover data from MapBiomas (2002-2022), predictive modelling using CA-Markov-MOLUSCE, and urban expansion analysis. The results show a 3% increase in urban coverage during the 2002-2022 period and a projected 12% growth by 2032, concentrated south of the Ruta Viva corridor and within the agricultural mosaic. Simulations show that slopes below 25° are more susceptible to urbanisation, while vegetation cover loss reaches 30% on the slopes of Ilalo Hill. This study provides a robust, replicable tool for anticipating urbanisation scenarios in Andean environments, guiding land management and environmental conservation strategies in regions of high urban pressure.

The Area-Based Approach (ABA) for modelling forest biomass with ALS data assumes perfect co-registration, but operational inventories often have spatiotemporal misalignments. This study isolates and quantifies the independent error contributions from temporal gaps and spatial co-location errors. The analysis uses a unique dataset from the Taiga Plains, Canada, featuring 163 re-measured field plots paired with repeated ALS acquisitions from the same sensor.

To assess temporal effects, we constructed scenarios with varying time-gap distributions. Symmetrical time gaps (SD 1.1 vs 2.5 years) increased RMSE by ~1 percentage point but did not add bias. In contrast, skewed distributions introduced significant systematic biases of 8.0 % (6.8 Mg ha⁻¹).

To assess spatial effects, we linked co-location uncertainty directly to plot-level neighbourhood heterogeneity. This was done by shifting the 20x20m ALS footprint over a 1m lattice and recalculating predictors. The resulting predictor variability (RMS(CV) 12.7%) was propagated through the model, implying a positional sigma of 10-15%. Monte Carlo simulations confirmed this spatial component is the dominant error source, contributing 2–4 percentage points to the ~22% baseline %RMSE.

Our findings show that while balanced temporal gaps are manageable, spatial co-location affected by the local heterogeneity is the most critical factor for robust ABA models.

Bathymetry information around islands, reefs, and shallow-water regions is critical for both navigation safety and environmental management. However, these areas often feature diverse substrate types and strong spatial heterogeneity, which makes it challenging to accurately retrieve fine-scale bathymetry from traditional medium-resolution satellite imagery. High-spatial-resolution (HSR) sensors, such as PlanetScope SuperDove (~ 3.7 m spatial resolution), offer the potential to capture more detailed spatial features, yet their relatively low signal-to-noise ratio (SNR) can lead to noisy retrievals, particularly over low-reflectance waters. To mitigate this issue, incorporating the spatial context of neighboring pixels while jointly utilizing the spectral information offered by low- and high-resolution sensors can enhance the stability and accuracy of HSR-based bathymetry retrievals. In this study, a UNet++ neural network with the spatial and channel squeeze & excitation (scSE) attention mechanisms (BathyUNet++) was employed to retrieve bathymetry from SuperDove imagery. To satisfy the patch-based input requirement of UNet++, the model was fully trained using two sources of data: clear-sky SuperDove image patches paired with Landsat-8-derived bathymetry and a limited set of ALB data. Validation results demonstrated that the model accurately retrieved bathymetry in regions independent of the training set.The proposed model and framework can be readily adapted to other HSR sensors, offering a promising approach for global HSR shallow-water bathymetry retrieval using multi-source satellite observations.

Despite its potential in forestry, agriculture, environmental monitoring, safety surveillance, and defence, real-time hyperspectral imaging (HSI) remains challenging in practice because of the high dimensionality of the data and limited onboard computational resources.

This work introduces a distributed HSI classification framework that integrates federated learning, a Self-learning Minimal Learning Machine classifier (SL-MLM), adaptive Kalman filter-based model fusion, and lightweight MQTT-based communication on Raspberry Pi edge devices and a laptop serving as the base station. Acting as local nodes, Raspberry Pis process HSI data row by row, update their models recursively, and only exchange compact model parameters and classification results with the base station. HSI data in its raw form remains local.

The findings suggest that the proposed local learning workflow can be implemented on Raspberry Pi devices, and Kalman-based fusion improves stability and consistency in comparison to individual local models. The method is feasible in scenarios where the number of labelled data points is restricted, as the SL-MLM classifier can be initialized with a mere handful of class-specific reference points. The research demonstrates a feasible, low-cost approach to distributed

embedded HSI classification and sensing.

Monitoring inland Water Quality Parameters (WQPs) is essential for managing freshwater ecosystems and assessing anthropogenic impacts (Mishra et al., 2017). Satellite remote sensing provides a cost-effective and large-scale approach for monitoring inland WQPs. However, most existing satellite sensors have limited spectral resolution, restricting their ability to capture subtle optical variations expressed by inland WQPs, and/or insufficient spatial resolution to yield valid water-only pixels in narrow rivers or nearshore zones (Ansari et al., 2025).

Recent advances in hyperspectral satellite technology have created new opportunities for inland WQP monitoring. The Wyvern Dragonette-001, launched in April 2023, provides hyperspectral imagery with a spatial resolution of 5.3 m and 23 spectral bands within the visible to near-infrared range (500–800 nm) (Ansari et al., 2025; Wyvern Dragonette, 2023). Given its novelty, the potential of such imagery for assessing WQPs in inland water remains largely unexplored. A recent review (Ansari et al., 2025) evaluating the sensor’s spectral resolution and signal-to-noise ratio for retrieving inland WQPs indicated that Dragonette-001 is suitable for estimating non-algal particles (NAP) and shows potential for chlorophyll-a mapping, although it is likely unsuitable for retrieving Colored Dissolved Organic Matter (CDOM).

This study reports on a practical test that assessed the feasibility of using Wyvern Dragonette-001 imagery to retrieve turbidity, Suspended Sediments (SS), and Dissolved Organic Carbon (DOC) in a portion of the St. Lawrence River, Québec, Canada.

Hyperspectral imaging produces vast data volumes that often exceed storage and transmission capacities on airborne and satellite platforms. This study systematically investigates the effects of lossy hyperspectral data compression on the scientific usability of the resulting data products. Using UAV-based HySpex acquisitions from the HyperThun’22 campaign, several state-of-the-art learning-based compression models were evaluated, including spectral, spatial, and spatio-spectral architectures. The analysis quantifies how compression-induced reconstruction errors propagate through the full pre-processing workflow, from raw digital numbers through radiometric calibration, geometric correction, and atmospheric correction to the final surface reflectance domain.

Results show that spectral models such as the Adaptive 1D Convolutional Autoencoder (A1D-CAE) achieve the highest fidelity, maintaining sub-degree spectral deviations and near-perfect structural similarity. In contrast, purely spatial or 3D convolutional models exhibit severe distortions that persist across all pre-processing levels. The findings demonstrate that lossy compression can be applied at the raw stage without compromising the integrity of reflectance products, provided that spectral correlations are explicitly modeled.

This work highlights the importance of selecting compression architectures consistent with sensor characteristics and pre-processing workflows and provides a quantitative foundation for future operational implementations of onboard hyperspectral compression in Earth observation missions.

Efficient nitrogen (N) management remains a major challenge for sustainable potato production, particularly on coarse-textured soils prone to nutrient leaching. This study investigates the use of Visible–Near Infrared to Short-Wave Infrared (VNIR–SWIR, 350–2500 nm) hyperspectral spectroscopy for non-destructive, in-season estimation of petiole nitrate nitrogen (PNN) under both field and laboratory conditions. Spectral data were collected using an ASD FieldSpec Pro spectroradiometer and processed through Savitzky–Golay smoothing, Standard Normal Variate normalization, and first-derivative transformation. Variable Importance in Projection (VIP) analysis was employed to identify N-sensitive wavelengths, and three predictive approaches—One-Dimensional Convolutional Neural Network (1D-CNN), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR)—were compared for their predictive accuracy. Calibration transfer using Piecewise Direct Standardization (PDS) was applied to harmonize field spectra with laboratory measurements. Results showed that the 1D-CNN achieved the highest predictive performance (R² = 0.90, RMSE = 0.22%), outperforming SVR and PLSR. PDS improved field-based predictions by reducing spectral discrepancies caused by illumination and canopy variability. The findings highlight the potential of hyperspectral spectroscopy combined with deep learning and calibration transfer techniques to provide accurate and scalable diagnostics of plant nitrogen status. This research supports the integration of proximal sensing and data-driven models for precision nutrient management in potato systems and broader agricultural applications.

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. The proposed framework integrates strip convlution layers with multi-size kernels in a dense connection to enhance stripe structural information expression 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.

This paper aims to evaluate the performance of ISODATA clustering for tree species classification using ultra-high-resolution multispectral data collected with Unmanned Aerial Vehicle. The study focuses on two sites in Żednia forest district near the city of Bialystok, northeastern Poland. The input data consist of 10-band multispectral orthomosaics with a resolution of 10 cm, acquired from an UAV platform equipped with a MicaSense RedEdge-MX dual camera and image-based Canopy Height Model. The classifications were conducted at two levels of forest detail: forest types, including two classes (broadleaf and conifer), and tree species, comprising four classes in Study Area 1 and ten species in Study Area 2. Multiple classifications were generated, testing different input parameters such as the number of clusters and various combinations of input data. For the first level of classification (forest type), overall accuracies range from 84,09% to 97,57% in Study Area 1 and from 82,31% to 92,74% in Study Area 2. At the second level of classification (tree species), overall accuracies vary from 70.73% to 91.77% in Study Area 1 and from 36,51% to 72,33% in Study Area 2. Overall, ISODATA demonstrates robust performance in classifying forest types in both study areas. However, performance in classifying tree species varies across different classes, with relatively high accuracies observed for certain species such as spruce, pine, oak, larch, and birch. The results underscore the potential of multispectral UAV data and unsupervised classification methods for accurately classifying tree species.

To enable non-destructive quantitative identification of mixed salts in mural plaster layers, hyperspectral data were collected from Na₂SO₄-CaCl₂ mixed-salt samples. Based on these data, a method integrating spectral preprocessing, feature-set construction, and machine-learning inversion was proposed. First, the original spectra were preprocessed using Savitzky-Golay smoothing and multiplicative scatter correction. A 0.6-order fractional-order derivative (FOD) was then introduced to enhance subtle salt-related spectral features. Subsequently, 30 single-band features were selected using a two-step strategy involving competitive adaptive reweighted sampling for preliminary screening and variable importance in projection for secondary screening. On this basis, dual-band and tri-band spectral indices were further constructed, and a combined-band feature set was formed by integrating the three feature sets. Gaussian process regression (GPR) was used to compare the inversion performance of different feature-input strategies for Na₂SO₄ and CaCl₂ contents. The results showed that the 0.6-order FOD achieved a favorable balance between feature enhancement and noise suppression. Among the evaluated feature-input strategies, the combined-band model showed the best predictive performance for both Na₂SO₄ and CaCl₂. These results indicate that integrating complementary information from feature sets with different dimensions can improve the stability and accuracy of mixed-salt inversion, providing a useful reference for the hyperspectral non-destructive quantification of mixed salts in murals.

Map archives, serving as crucial cultural heritage documenting historical spatial information, face severe challenges in long-term preservation. To evaluate the feasibility of deacidification technology in the conservation of map archives, this study utilized 41 severely acidified early 20th-century map archives as samples. These were treated using a specific non-aqueous deacidification technology, and changes in pH value, color difference (ΔE), and inks stability before and after treatment were analyzed. The results indicate that after deacidification, the paper pH value significantly increased from an average of 4.48 to a range between 8.24 and 8.87. The color change was minimal, with an average color difference ΔE of only 1.62. This study verifies that the deacidification technology is suitable and effective for the deacidification treatment of acidified paper-based map archives, providing a safe and reliable method for preserving their cultural value.

To address the challenges of large initial pose discrepancies in grotto point clouds acquired from multiple sources, complex local geometric structures, significant noise interference, and the tendency of traditional ICP algorithms to fall into local optima, a high-precision point cloud registration method is proposed by integrating feature extraction with the collaborative optimization of coarse and fine registration. This method first performs point cloud preprocessing through voxel downsampling and outlier removal; it then extracts stable feature regions based on normal vector estimation and curvature analysis, and constructs feature representations using FPFH descriptors; building on this, the K-4PCS algorithm is employed to perform coarse registration and obtain optimal initial transformation parameters, followed by fine registration using an improved ICP algorithm combined with KD-tree-based search optimization. The proposed method was validated using the STANFORD DRAGON dataset and the point cloud of the Buddha head statue from Cave 18 of the Yungang Grottoes. The results indicate that the proposed method effectively improves the convergence speed and accuracy of point cloud registration. It demonstrates good stability and applicability in complex cave heritage scenarios and can provide methodological support for the fusion of multi-source point clouds in the digital preservation of cultural heritage.

To address the lack of suitable methods for automatic 2D line drawing generation from grotto wall mesh models, as well as the difficulty of existing methods in balancing structural representation and detail preservation, this paper proposes a line drawing generation method based on depth map and normal map fusion. The method first orthographically projects the 3D model into a depth map and a surface normal map, then constructs an initial line drawing pipeline based on projected edge fusion. A layered optimization strategy is further introduced to improve detail representation and result stability. Experiments on the mesh model of the north wall of Cave 18 at the Yungang Grottoes show that the projected edge fusion method is more suitable for overall structural representation, while the layered optimization method performs better in preserving weak structures and fine details. The proposed method effectively improves the quality of automatic 2D line drawing generation for grotto wall surfaces.

Rock-cut cave temples, such as the UNESCO World Heritage site of Yungang Grottoes, represent invaluable cultural heritage facing severe deterioration. Traditional monitoring methods are often slow, subjective, and inadequate for large-scale, long-term analysis, creating a critical gap in effective conservation.To address this challenge, we developed an automated framework for identifying surface deterioration features on stone carvings using deep learning. Our approach leverages a novel multi-source image dataset, combining historical and modern imagery of the Yungang Grottoes. We propose an enhanced model based on the YOLO architecture, featuring a synergistic semantic and spatial perception mechanism that significantly improves the detection of subtle features like peeling and cracks.The model was trained to recognize three key deterioration types: peeling, crack, and human damage. On-site deployment and testing in the authentic cave environments demonstrated excellent performance, achieving high recognition confidence for cracks (87.5%), peeling (85.2%), and human damage (81.3%). This study provides a powerful new tool for the quantitative monitoring of stone carvings, offering a scientifically-informed pathway for practical and proactive conservation strategies at heritage sites worldwide.

Most of the grotto temples have undergone long-term weathering and multiple repainting campaigns, so accurate identification of the composition and spatial abundance of surface pigments is an important foundation for pigment characterization and conservation research. This study focuses on the dome of Yungang Grotto 7. Data were acquired using a three-dimensional (3D) hyperspectral multimodal digital acquisition system and the Analytical Spectral Devices (ASD) field spectroradiometer. The workflow consisted of two stages: pigment identification and abundance inversion. In the pigment identification stage, a normalized weighted identification method integrating Spectral Angle Mapper (SAM) and the Normalized Difference Spectral Index (NDSI) was proposed based on mineral pigment reflectance curves measured by the ASD field spectroradiometer. In the abundance inversion stage, Fully Constrained Least Squares (FCLS) was applied to estimate pigment proportions in mixed pixels under non-negativity and sum-to-one constraints. The results show that the green pigments are most likely malachite and Paris green, the red pigments are most likely hematite and laterite, and the black pigment is most likely carbon black. The interwoven distribution of Paris green and traditional mineral pigments provides material-science evidence for modern repainting and restoration in this area. Nonlinear mixing may occur on rough and weathered grotto surfaces. However, under the current data conditions, its influence on abundance inversion remains unclear. Therefore, Kernel Fully Constrained Least Squares (K-FCLS) was additionally introduced as a reference nonlinear model for qualitative comparison with FCLS.