Conference Agenda

Gorgan Bay in the southeast of the Caspian Sea faces significant issues with water volume reduction and water quality deterioration. The turbidity levels of this water body have increased recently owing to the ongoing decline in the Caspian Sea level and the increase in human activity. In this study, to monitor water quality of the bay, various machine learning models were used to retrieve turbidity levels from Sentinel-2 satellite imagery. In situ turbidity measurements acquired throughout the bay were correlated with Sentinel-2 reflectance data. A statistical evaluation was conducted to ascertain the prospective band combinations for estimating turbidity. Four regression methods, including Multiple Linear Regression (MLR), Random Forest (RF), Classification and Regression Trees (CART), and Gradient Tree Boost (GTB), were implemented to estimate turbidity levels using six different input scenarios. These models were tested on unseen test data, and it was found that the CART model with RMSE = 7.89 FTU, R² = 0.93, and Nash-Sutcliffe efficiency (NSE) = 0.74 exhibited superior performance. The generated turbidity maps across the bay showed sediment plumes next to southeastern river mouths, indicating increased turbidity levels in these areas compared to the rest of the bay, revealing intra-bay variability due to tidal and discharge dynamics. The applied methodology demonstrated superior performance compared to conventional empirical models in turbid coastal environments. The results indicated that the machine learning approaches coupled with satellite data provides water resource managers with a cost-effective and real-time tool for coastal water quality monitoring.

The formation of pit lakes in decommissioned open-pit mines has raised concerns regarding long-term water quality. Turbidity, a key indicator of suspended particulate matter, influences water clarity and aquatic ecological processes. This study estimates surface turbidity in the Águas Claras Mine (MAC) pit lake in Nova Lima, Brazil, using satellite imagery and in situ data to generate a continuous time series and assess compliance with thresholds established by current Brazilian environmental legislation (CONAMA Resolution No. 357/2005). Landsat 5 and 8 imagery were used to derive a spectral turbidity index. Based on the temporal overlap between satellite and field data, a linear regression model (R² = 0.77) was developed and applied to extend the turbidity time series. The results indicate that turbidity values remained below the legal limits for Class 1 freshwater. Higher turbidity levels were observed during the initial filling phase, associated with exposed slopes, as well as episodic increases during the rainy season due to sediment runoff. Over time, progressive revegetation and minimal anthropogenic disturbance contributed to the stabilization of water quality conditions. The integration of in situ measurements and remote sensing proved to be an effective approach for monitoring water quality in post-mining environments, supporting both environmental liability assessment and closure management.

Harmful algal blooms caused by Margalefidinium polykrikoides have intensified in Korean coastal waters, yet existing bio-optical models are not able to reproduce the species-specific spectral features required for quantitative bloom assessment. This study develops a dedicated semi-analytical bio-optical model by integrating multi-year field measurements collected from six campaigns between 2013 and 2022, including hyperspectral above-water radiometry, laboratory absorption spectra, and chlorophyll-a (Chla) observations.

The model formulation follows a standard absorption–backscattering reflectance framework, in which total absorption is decomposed into water, phytoplankton, NAP, and CDOM components, while phytoplankton backscattering is parameterized using two optimized species-dependent parameters. An iterative inversion procedure identifies the optimal backscattering structure by minimizing the spectral mismatch between modeled and measured hyperspectral Rrs. In addition, an empirical red-edge term is introduced to capture the distinct fluorescence-associated peak near ~700 nm that characterizes high-biomass M. polykrikoides waters.

The resulting model accurately reconstructs observed Rrs across low to high Chla conditions, reproducing key features such as strong blue absorption, the secondary blue rebound, and the pronounced red-edge peak. Comparisons with GIOP and Karenia-based models show substantially improved performance, particularly under extreme bloom conditions.

This work provides the first validated species-specific bio-optical parameterization for M. polykrikoides and offers a practical pathway for satellite-based HAB monitoring using upcoming hyperspectral missions such as PACE and GLIMR. The framework is extendable to additional HAB species and supports future development of physics-based, species-resolved coastal water-quality retrievals.

Coastal zones are highly vulnerable to multiple hazards, including tsunami, shoreline erosion, coral reef degradation, and escalating impacts of sea-level rise. These concerns illustrate the urgent need for accurate and high-resolution geospatial data in coastal areas are required to support coastal risk assessment and management. A seamless and accurate coastal digital elevation model (DEM) is a foundational dataset to support these needs.

However, the development of a seamless land-sea elevation surface remains challenging. The intertidal zone often forms a critical data gap between land DEMs and bathymetry grids. To address these limitations, the use of multi-sensor geospatial data has grown considerably in coastal science and hydrography, such as structure-from-motion (SfM) photogrammetry and satellite-derived bathymetry (SDB). Assimilating SfM and SDB offers a viable pathway for constructing seamless coastal DEM.

Therefore, understanding the quality of SfM and SDB data before integration is a critical step. This study addresses this gap by evaluating the vertical accuracy, effective spatial resolution, and internal consistency of SfM-derived coastal topography and SDB-derived shallow-water bathymetry in a challenging coastal environment commonly found in Indonesian waters, i.e., coral reef islands. In this study, the area of interest is Karimunjawa and Kemujan Islands, located in the Java Sea, Indonesia, approximately 80-90 km from the mainland.

Based on our preliminary results, SfM provides high spatial detail depths but introduces short wavelength oscillation while SDB shows smoother depth gradients. In addition, several depths derived from SfM and SDB indicate different vertical reference levels.

This study evaluates the classification performance of Sentinel-2 multispectral and EnMAP hyperspectral imagery across three Canadian agricultural sites. Using Random Forest with recursive feature elimination, we assess whether EnMAP’s spectral richness provides measurable improvements for operational crop mapping. Results show comparable overall accuracies, with EnMAP offering advantages for spectrally complex crop types. Findings highlight the potential and practical limitations of incorporating satellite hyperspectral data into national agricultural monitoring workflows.

Agricultural land in Slovakia has undergone a significant transformation over the past two decades, but the extent and direction of this change remain insufficiently quantified at the national level. This study provides the first spatially explicit assessment of agricultural land loss using Land Parcel Identification System (LPIS) records (2004–2022) in combination with Sentinel-2-based land cover classification. By differentiating the LPIS, we identified polygons that were lost from the agricultural land register; these polygons could represent abandoned agricultural land as well as areas converted to other uses. These polygons were classified into four land cover classes using a Random Forest model trained on Sentinel-2 spectro-temporal metrics and cleaned LUCAS 2022 samples (F1 = 0.867), with additional filtering applied to separate grasslands from shrubs based on vegetation height. The results show that more than 1,000 km² of originally suitable agricultural land has been converted to other land cover types. Forest expansion accounts for 834 km², while 298 km² has been converted to shrubland and 553 km² remains as grassland. Non-forested areas, including buildings and infrastructure, cover an area of 258 km². Only 17 km² of formerly agricultural land remained as actively utilised arable land. These findings indicate that agricultural land abandonment, ecological succession, and urbanisation are the primary causes of agricultural land loss in Slovakia. The research presented provides important data confirming that the loss of agricultural land is extensive and largely threatens habitats with high biodiversity, highlighting the urgent need to harmonise strategies across agriculture, the environment, and land-use planning.

Agricultural drought affects crop productivity and threatens food security. This study presents the Hierarchical Robust Combined Drought Index (HRCDI) for operational agricultural drought monitoring, based on Earth Observation (EO) data freely available in Google Earth Engine. The HRCDI integrates four complementary indicators—Standardized Precipitation Evapotranspiration Index (SPEI3), Soil Moisture (SM), Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI)—using a hierarchical fuzzy logic framework that reflects the progression of drought impacts from climatic anomalies to vegetation stress. To ensure robustness, monthly anomalies of SM, LST, and NDVI were computed through a robust z-score formulation based on median and NMAD, which reduces the influence of outliers. The HRCDI was applied to the Province of Foggia (southern Italy), one of the main durum wheat production areas in Italy, over the period 2017–2022. HRCDI outputs were aggregated at the municipality scale and validated against independent datasets, including durum wheat yield statistics (2006–2022), SPEI3 provided by the Institute of Services for Agricultural and Food Market (ISMEA), and reports from the European Drought Impact Database. The HRCDI effectively captured the severity and spatial extent of major drought events, particularly in 2017 and 2022, which corresponded to documented yield losses of −5% and −22%, respectively. Results highlight the scalability, operational relevance, and transferability of the HRCDI for supporting drought early warning and agricultural risk management. The HRCDI framework could be applied to other regions and integrated with higher-resolution satellite data to enhance drought monitoring in line with the objectives of the Common Agricultural Policy.

Fall Armyworm (FAW) is a major pest, threatening maize production and food security. FAW preferentially attacks nitrogen-rich maize due to its nutritional composition, which accelerates larval development. Understanding the effects of FAW infestation on maize growth and nutrient status is critical for effective crop management. This study (i) examines the impact of FAW infestation on maize using key physiological metrics, including fresh/dry weight, chlorophyll content (as measured by SPAD), leaf area index (LAI), stem length, and leaf dimensions (length, width, and area); (ii) investigates differences in carbon, hydrogen, nitrogen, and sulphur concentrations between healthy and infested maize, and (iii) analyses spectral variability between healthy and infested maize using leaf-level hyperspectral data. Hyperspectral data at the leaf scale were used to identify and distinguish the spectral reflectance between healthy and infested FAW maize crops. Results indicate that infested crops exhibit lower fresh weight, reduced LAI, shorter stems, and smaller leaves compared to healthy crops, highlighting a substantial negative effect on above-ground biomass and overall crop vigour. Infested crops showed higher nitrogen levels than healthy crops. This trend could be attributed to nitrogen redistribution following FAW damage, where nitrogen decreases in attacked leaves and increases in roots, with partial recovery after the pest moves on to another crop. The study establishes a methodological framework for linking laboratory, field, and remote sensing approaches, providing a foundation for future predictive modelling of pest impacts on maize nutrient content and productivity.

Solar-induced fluorescence (SIF) has become a promising remote sensing proxy for photosynthetic activity and thus plant health, but its broad application across vegetation types and climate regimes remains underexplored. Here, we present the first continental-scale assessment of seasonal SIF signatures for 33 vegetation types across 24 climate zones in the contiguous United States, enabled by a new open-access visualization tool. The analysis uses TROPOMI satellite SIF data (2019-2021), along with MODIS-derived gross primary productivity (GPP), normalized difference vegetation index (NDVI), and vapor pressure deficit (VPD). Our results show that SIF has consistently stronger and more reliable correlations with GPP than NDVI across vegetation types and environmental conditions. This relationship remains robust even under high VPD conditions (except for several perennial crops), confirming the ability of SIF to track productivity even in dry environments. While NDVI retains structural sensitivity, it often decouples from GPP under stress, particularly in arid climates and perennial crops. We also identify clear differences in SIF-NDVI and GPP-NDVI relationships by vegetation type and climate, with NDVI showing limited responsiveness to dynamic changes in canopy physiology. Despite the coarse spatial resolution of TROPOMI, these results demonstrate the feasibility of constructing climate-specific SIF signatures for agricultural and ecological monitoring. By identifying these climate-specific signatures at the continental scale, this work highlights the value of SIF for climate-smart crop management, productivity assessment, and satellite-based ecosystem modeling.

Accurate estimation of gross primary production (GPP) is fundamental for quantifying the terrestrial carbon cycle. However, coarse-resolution products often fail to capture fine-scale spatial variations in carbon uptake across heterogeneous landscapes. While recent studies have begun to employ 10 m Sentinel-1 and Sentinel-2 imagery, they typically reduce these data to pixel-wise spectral indices, discarding the two-dimensional spatial structure (canopy architecture, land-cover transitions, within-stand heterogeneity) that the imagery encodes. This study investigates whether explicitly exploiting this spatial context via convolutional neural networks yields robust, transferable gains over tabular machine-learning baselines. We curate a quality-controlled dataset of 23,528 eight-day multi-sensor composites from 222 AmeriFlux sites (2015–2025), evaluated under site-wise cross-validation, temporal generalisation, and geographic transfer to an 18-site upper Midwest forest holdout. Under temporal transfer to unseen years (2023–2025), the best convolutional model achieves R² = 0.77 and RMSE = 1.95 gC m⁻² d⁻¹, an 18.6% RMSE reduction over ridge regression (R² = 0.65, RMSE = 2.40 gC m⁻² d⁻¹). Although this advantage narrows under geographic transfer to structurally novel regions (R² = 0.59 vs. 0.54), the convolutional models still outperform all tabular baselines. Spatial structure at 10 m therefore supports more robust temporal generalisation than spectral aggregates alone.

Accurate gross primary productivity (GPP) estimation is crucial for understanding ecosystem function and the global carbon cycle. Remote sensing offers promising GPP proxies, including solar-induced chlorophyll fluorescence (SIF) and the structural proxy NIRvP. However, their performance and underlying drivers of effectiveness vary significantly across biomes. This study comprehensively evaluated the accuracy and limitations of SIF and NIRvP against flux GPP across diverse biomes (CRO, GRA, DBF, ENF), also investigating physiological and structural controls on LUE.

We found that proxy performance was highly biome-specific. Notably, the removal of canopy escape probability (fesc) from observed SIF (SIFobs) to derive total emitted SIF (SIFall) did not consistently improve, and sometimes even diminished, its correlation with GPP, particularly in CRO and GRA. Furthermore, we elucidated distinct dominant controls on seasonal LUE variations: apparent SIF emission yield (ΦF×fesc) was paramount in ENF, while canopy structure (fesc) predominated in CRO, GRA, and DBF. Seasonal analysis in ENF further revealed a temporal decoupling, with fesc decline lagging LUE in winter, and ΦF failing to track autumnal LUE reductions. These findings underscore the biome-specific necessity for optimal GPP proxy selection, establishing a robust scientific foundation for improved remote sensing monitoring.

Accurate quantification of gross primary productivity (GPP) is essential for understanding carbon dynamics under climate change. However, satellite-based GPP estimates face spatial–temporal trade-offs, limiting accuracy in heterogeneous landscapes. To overcome this challenge, we proposed a novel framework named UNified, high-resolution Intelligent carbon QUantification and Explanation (UNIQUE), which produces daily 30 m GPP maps by integrating spatial relationships between 500 m MODIS and 30 m Landsat imagery. UNIQUE consists of two components. First, two AI models were trained using MODIS- and Landsat-based vegetation indices combined with meteorological reanalysis data and validated with 309 eddy-covariance flux tower observations across the Northern Hemisphere. The Light Gradient Boosting Machine (LGBM) showed the best performance, achieving r = 0.80 and RMSE = 2.47 gC/m²/day for MODIS-based GPP, and r = 0.83 with RMSE = 2.43 gC/m²/day for Landsat-based GPP. Second, a diffusion-based deep learning model was used to downscale MODIS-based GPP to 30 m resolution. The diffusion model from MODIS to Landsat GPP exhibited good performance, demonstrating an RMSE of 2.12 gC/m²/day for the testing sites. The proposed approach enabled the analysis of spatiotemporal characteristics of GPP across different plant functional types, facilitating enhanced high-resolution carbon flux monitoring in diverse ecosystems.

Insights into crop productivity have long been of great interest to almond growers, as they enable effective planning to optimise economic returns. Advances in sensor technology have made it possible to collect hyperspectral imagery, which captures detailed information across a continuous range of wavelengths and has become a powerful tool for assessing crop physiological status. Solar-induced chlorophyll fluorescence (SIF), along with other plant pigments and structural traits retrieved through radiative transfer modelling, can effectively track crop photosynthetic activity. However, the ability to quantify SIF is strongly influenced by the spectral resolution of the sensor.

This study examines how the spectral resolution of airborne hyperspectral sensors affects the ability to explain yield variability in a commercial almond orchard, by comparing SIF derived from the 760 nm and 687 nm oxygen absorption bands at different spectral resolutions.

Green roofs (GRs) are constructed systems that replicate natural ecosystems and provide runoff reduction, cooling effect, habitat support and improved air quality services. Over 1,000 GRs have been constructed in Toronto since the GR Bylaw was enacted. As they are dispersed and primarily small-scale stormwater assets on private properties, it is crucial yet difficult to assess these roofs' condition to ensure they continue to deliver the desired advantages and adhere to maintenance regulations. To maintain green stormwater infrastructures in ideal conditions, it is advised that the vegetation be maintained with 80% coverage. GR vegetation experiences plant loss, water stress, and other maintenance concerns requiring regular inspections. This study presents a framework to enable remote assessment of green roof conditions utilizing Sentinel-2A satellite imagery, which captures images every five days. This method overcomes logistical challenges associated with drone imagery inspections, which are limited in frequency and require permits. The study was conducted using Google Earth Engine, focusing on the intra- and inter-annual variation of four GR modules. The study assessed the vegetation health from 2018 to 2025 using NDVI, EVI and NDMI, highlighting the long-term dynamics and distribution of GR vegetation. The results present the effectiveness of NDVI, EVI, and NDMI in assessing plant coverage and moisture content, with low NDMI being an important factor resulting in low NDVI and EVI. The study contributes to the potential of satellite images for scalable and continuous monitoring of GRs and supports efficient and complementary inspection.

Accurate large-scale dense 3D reconstruction is fundamental for geospatial mapping, robotics, and autonomous navigation. Conventional photogrammetric workflows can reconstruct environments from ground-level imagery but often suffer from cumulative drift over kilometer-scale trajectories and require extensive calibration. Recent advances in feed-forward 3D reconstruction, notably the Visual Geometry Grounded Transformer (VGGT), have demonstrated the ability to generate dense point clouds directly from RGB images without explicit optimization. VGGT jointly estimates camera poses, depth, and dense geometry from multiple uncalibrated frames in a single forward pass. However, its scalability is limited by two factors: (1) the lack of absolute metric scale and (2) high GPU memory demands.

Many national mapping agencies (e.g., USGS, IGN, Ordnance Survey) have released Airborne Laser Scanning (ALS) datasets covering vast urban and rural areas. These high-quality aerial point clouds provide globally consistent, metrically referenced data that can serve as external constraints for ground-level reconstructions. Building upon this opportunity, we propose VGGT-ALS, a framework that leverages open ALS point clouds to enable VGGT-based systems to produce large-scale, metrically accurate dense maps from mobile mapping imagery.

High-precision vehicle localization in GNSS-denied urban areas requires alternatives to costly HD maps. In this paper, we present a novel framework for feature extraction and benchmark generation to enable high-precision localization using abstract LoD2/DTM maps as a replacement for HD maps. Our first contribution, a semantic-geometric pipeline, processes dense LiDAR and camera data to extract map primitives. This is accomplished by a RANSAC-fitted ground plane extraction step, followed by a semantic filter that discards dynamic objects. Finally, geometric clustering (HDBSCAN) and RANSAC plane fitting isolate large-scale vertical facades. Our second contribution, a multi-stage GT generation framework, resolves annotation ambiguity using a Human-In-The-Loop (HITL) system. A robust 2D pose is computed by finding the geometric median of bootstrapped transformation samples on the SE(2) manifold, which is then refined to a 6-Degree-of-Freedom pose via point-to-plane ICP, before being validated by a human for a final check. We evaluated our feature extraction pipeline against the generated benchmark, achieving 95.04% precision and 83.74% recall. An analysis of this performance shows the pipeline correctly rejects small, ambiguous features while achieving high recall on all large, stable features, proving its suitability for a robust localization filter.

As civil infrastructure in South Korea ages, the demand for systematic monitoring has grown. While Unmanned Aerial Vehicles (UAVs) provide a safer alternative to manual inspections, Global Navigation Satellite System (GNSS) signal degradation beneath bridge structures remains a critical barrier to autonomous flight. Unlike existing hardware-centric or SLAM-based solutions that require high costs and computational overhead, this study proposes a robust, algorithm-based path planning methodology using a 3D spatial grid framework.

Two strategies were evaluated through field tests at the Bukhangang Bridge: the Photography Point Method (PPA) and the GNSS Non-Shadowing Area Method (WPS-GNSA). Results demonstrated that WPS-GNSA significantly enhances signal reliability by strategically positioning waypoints outside electromagnetic shadow zones. At an optimal 19-meter separation distance, WPS-GNSA maintained GNSS Level 5 connectivity—the threshold for recommended autonomous flight—for 9.4% of the duration and Level 4 or higher for 79.7%, whereas the PPA peaked at Level 4.

These findings indicate that WPS-GNSA enables reliable autonomous inspections using standard commercial drones without specialized hardware modifications. While the current model relies on pre-existing digital blueprints, future research will integrate AI-based navigation and real-time environment perception to enhance scalability and adapt to dynamic obstacles in complex infrastructure environments.

Accurate and reliable positioning is fundamental for the development of a wide number of applications. Despite in most of the regular working conditions the use of a GNSS receiver is sufficient for properly solving the problem, determining a reliable solution in challenging conditions can be difficult. In such conditions, exploiting the information shared by different sensors and platforms can be useful for reliably determining the platforms' positions. In this work, both ground and aerial platforms are considered: each platform is assumed to be provided with communication capabilities, which can be exploited to share its knowledge. Since GNSS positioning is usually less effective at ground level than on a flying platform, the aerial platforms are assumed to be provided with good GNSS-based positioning information. Instead, GNSS is assumed to be unavailable to the ground vehicles, which, instead, can use LiDAR/visual odometry for dead-reckoning positioning, UWB inter-platform ranging for relative positioning, and camera-based positions, provided by aerial platforms, for assessing their georeferenced positions. This work focuses on assessing the positioning performance when exploiting vision-based information about the georeferenced ground vehicle positions from a camera mounted on a UAV. The camera acquired oblique views of the scene while moving over the case study area during the test. YOLO was used to detect cars from the image frames and the vehicle coordinates have been extracted from 3D reconstructions obtained from the MoGe-2 network. Average errors at meter level on the determined georeferenced coordinates were obtained when combining UWB vehicle-to-vehicle ranges with MoGe-2 reconstructions.

Reliable positioning and mapping in dense urban environments remain challenging due to signal blockage, multipath, and dynamic scenes. Progress on multi-sensor integrated positioning and visual/lidar SLAM has been driven by open datasets, yet most existing resources are either perception-centric with limited raw navigation data, focused on controlled environments, or built around outdated software platforms and/or data formats. In this paper, we present the Melbourne Multi-Sensor Urban Positioning and Mapping Dataset, a new resource targeting urban vehicle navigation and mapping tasks. The dataset was collected using a custom mobile mapping platform equipped with a tactical-grade INS, a survey-grade Leica GNSS receiver, a low-cost UBLOX GNSS receiver, a high-resolution Ouster OS1 128 lidar, and four industrial FLIR cameras providing 360° coverage. Seven data collection trips were recorded on dynamic streets in several inner suburbs of Melbourne, including multiple closed loops and a repeated route with day–night variation. For better compatibility and future-proofing, all raw data are provided as standard ROS2 message streams in MCAP format, complemented by commonly used individual formats and GNSS products for multi-sensor integrations. We benchmark three GNSS--based positioning packages (RTKLib, Net_Diff and Ginan) and four state-of-the-art lidar(-inertial) odometry/SLAM methods (FAST-LIO2, KISS-ICP, KISS-SLAM and PIN-SLAM), demonstrating the applicability and compatibility of our dataset for modern positioning and mapping software pipelines. The dataset is designed as a robust, ROS2-native testbed for research on GNSS/IMU/lidar/camera fusion for the testing and validation of vehicle positioning and mapping in urban environments, which is available open-source at https://github.com/zjjdes/melbourne_dataset.

Accurate and robust localization is essential for autonomous vehicles and mobile robots operating in complex, dynamic environments. However, existing learning-based LiDAR-inertial odometry (LIO) methods typically rely on simple weighted fusion or purely global attention, which may not fully exploit cross-modal complementarity. In this paper, we propose CMLGF-LIO, a cross-modal local-global fusion framework that improves LIO accuracy and robustness. At the local level, we design a Local Split-Attention (LSA) module that injects IMU-derived motion priors into local LiDAR feature groups and adaptively allocates attention weights, suppressing redundant information while preserving discriminative local geometry for fine-grained fusion. At the global level, we introduce a Global MLP-Mixer (GMM) module that aligns LiDAR and IMU token sequences and models global cross-modal interactions using an MLP-Mixer backbone. Experiments demonstrate that CMLGF-LIO is more robust than learning-based baselines under challenging conditions, and ablation studies validate the effectiveness of the proposed local-global fusion strategy.

This paper presents a low-cost mobile mapping system built on a consumer vehicle (2026 Tesla Model Y) equipped with a roof-mounted Velodyne VLP-16 LiDAR and three post-processed kinematic (PPK) GNSS receivers. A processing pipeline was developed that fuses scan-to-scan LiDAR registration via KISS-ICP with multi-receiver PPK trajectories to produce georeferenced 3D point clouds. The system was tested on a 2 km loop on the Ohio State University campus. KISS-ICP achieved a registration fitness of 1.0 across all 3,400+ frames, producing locally crisp point clouds. A yaw-only Procrustes alignment followed by interactive 7-parameter refinement maps the SLAM trajectory into an East-North-Up geodetic frame. We document the complete pipeline architecture, including automated GPS time synchronization, multi-receiver vehicle pose estimation, and a streaming LAS export capable of handling 70+ million points. We systematically evaluate ten post-hoc trajectory correction strategies and identify a fundamental trade-off between inter-frame consistency (point cloud crispness) and absolute geodetic accuracy. The primary unresolved challenge is a staircase artifact caused by ~40 m of accumulated SLAM drift over the loop, which cannot be corrected without degrading local registration quality. We conclude that loop closure detection and pose graph optimization within the SLAM pipeline are necessary to resolve this tension and outline a path toward survey-grade mobile mapping from consumer vehicle platforms.

The Virtual Campus Project at the University of Cologne (UoC) has as a main objective the creation of a highly detailed 3D model of the university campus and its publication and distribution through OGC 3D Tiles. Further objectives include the development of integrated applications leveraging this 3D model, such as a web-based 3D viewer, game engine-driven geospatial augmented reality (GeoAR) and virtual reality (VR) experiences, and an indoor positioning system utilizing 3D building models indoor geometries. This paper focuses on and details the methodology for developing and implementing the georeferenced 3D model and establishes an Open Geospatial Consortium (OGC) 3D Tiles-compliant Spatial Data Infrastructure (SDI). The main result is a Tool Suite or Software Framework and the description of the tool pipeline or workflows for collecting, creating and modelling the 3D geospatial data and publishing it as OGC 3D Tiles data. This framework ensures campus-wide 3D data accessibility through 3D Tiles standard clients, including Desktop GIS like ArcGIS or QGIS, game engines like Unity, Unreal or O3DE and Webmapping libraries like MapLibre, three.js or CesiumJS.

The assessment of sidewalk infrastructure for accessibility compliance is an important task in urban planning; however, traditional methods are often manual, subjective, and resource-intensive. This paper introduces BirdCV-LiDAR, a multimodal data fusion framework for an automated assessment of sidewalk infrastructure. The proposed approach integrates high-resolution bird's-eye-view (HR-BEV) imagery with aerial LiDAR point cloud (ALPC) data to automatically detect, measure, and assess sidewalk features for compliance with accessibility standards. By combining YOLO-oriented bounding box (OBB) models with precise LiDAR-based elevation data, the framework enables accurate dimensional and slope evaluations of sidewalk features, such as crosswalks and truncated domes. Validation with a 12-inch inclinometer shows that LiDAR-based slope measurements achieve 84.7% accuracy, with a root-mean-square error (RMSE) of 0.1152 meters for crosswalk width measurements. The framework achieves 81.0% accuracy in determining ADA-PROWAG compliance, providing an adaptable, expandable solution for improved urban accessibility assessments.

This paper proposes a micro-scale walkability metric based on harmonised indicators that supports pedestrian route planning, which prioritises pleasant environments alongside distance efficiency. The employed method quantifies street segments and crossings using geospatial indicators, including pavement width, slope, shade, adjacency to traffic, park context, and crossing type and width. Indicator values are transformed to percentile ranks to harmonise heterogeneous inputs and are aggregated into a single edge-level walkability score on a 0 to 1 scale. The score is integrated into a routing cost function that reduces edge cost with higher walkability, which favours calmer, greener, and wider links while bounding detours relative to the shortest path. The method also accommodates the incorporation of street-level perceptions through a structured survey instrument and a confidence-weighted fusion scheme.

The results show various spatial patterns. Central areas and park-adjacent segments exhibit higher scores, while steep, narrow, and traffic-exposed links score lower, and several suburban and foothill districts display reduced walkability. The comparison with a distance-only baseline shows selection of quieter alignments with modest length increases, indicating potential gains in perceived pleasantness.

Urban areas are continuously expanding outward with economic development and demographic growth, while simultaneously growing higher vertically. However, few efforts have been made to evaluate the impact of development priorities in different regions on urban sustainability, which limited our understanding of how urbanization has been affected by imbalanced evolution rhythm. Here we developed a 3D structure-based approach to assess volumetric urban expansion, as well as a refined evaluation system for assessing urban imbalanced growth trends. Results show that the 3D expansion patterns of urban areas exhibited significant heterogeneity globally. As urbanization accelerates, urban areas in the Global South are showing a trend of faster expansion accompanied by faster vertical growth. In addition, imbalanced growth types across different dimensions are significantly more complicated in the Global South than in the Global North, indicating the variance of development priorities is greater in the Global South. Furthermore, the imbalances are intensifying over time, as indicated by the temporal indices. Our study enhances the understanding of urban 3D patterns and imbalanced urban evolution, providing crucial insights for more balanced urbanization especially in the Global South.

Urban green spaces are important for residents’ physical and mental health, but green visibility is difficult to quantify in high-rise, high-density cities, especially across different height levels. To address this problem, this study proposes a stratified green visibility framework based on airborne LiDAR point clouds and a voxel model. Using the Dutch AHN5 dataset, the study area was converted into a unified 3D voxel space and classified into trees, grass, buildings, ground, and empty space. A voxel-level penetration probability model based on the Beer–Lambert law was introduced to represent the semi-transparent blocking effect of tree canopies, improving upon conventional binary visibility models. Multi-directional line-of-sight (LOS) tracing was then applied to calculate green visibility (GVI) and spatial openness (SOP) at different height layers. The results show that GVI is generally high around parks, large green spaces, and some enclosed courtyards, but its contribution from street trees is limited. Vertically, GVI decreases with height, while SOP tends to increase. Combining the two indicators helps identify different spatial types with distinct visual characteristics. The study demonstrates that airborne LiDAR, combined with voxelization and probabilistic 3D simulation, can effectively capture the vertical variation of urban GVI and support large-area assessment in high-density residential environments.

Accurate building-level mobile device positioning is critical for fine-grained location-based services and human activity analysis, as people spend 80–90% of time indoors. Existing techniques rely on dedicated infrastructure or dense fingerprinting, limiting scalability. This study proposes a lightweight, infrastructure-free framework integrating two core modules: 1) Indoor/outdoor classification via a random forest model trained on a multi-scene sample library, using satellite, Wi-Fi, Bluetooth, and cellular sensor features with similarity-guided training selection; 2) Building matching through a Bayesian inference model leveraging three-scale spatial features (device, building, area) and prior knowledge from anonymous crowdsourced data. Validated in Beijing, Nanjing, and Xi’an, the framework achieves over 90% overall precision for indoor/outdoor classification and ≥70% precision for building matching with satellite or Wi-Fi features alone. It requires no extra infrastructure or extensive labeled data, offering a scalable solution for smart city applications like population analytics, emergency response, and context-aware services across heterogeneous urban regions.

In Japan, the combined effects of declining birth and marriage rates have accelerated population decline, leading to spatial porosity and fragmentation in urbanised areas: a phenomenon known as “Urban spongification”. This study analyses local population distributions in order to identify localised low-population areas embedded within densely populated urban environments, with the aim of understanding spatial porosity and fragmentation in Osaka Prefecture. A multi-scale spatial autocorrelation approach was applied to detect the spatial extent of localised low-population areas, and results were compared between 1995 and 2020. The analysis further examined how the formation and change of localised low-population areas differ across Use Districts and according to long-term land-use transition histories. The findings reveal pronounced spatial variability within districts that cannot be captured by conventional population density metrics alone. The study demonstrates that the emergence, persistence, and transformation of localised low-population areas are closely related to zoning regulations and historical land-use processes. These results provide insights into the spatial processes contributing to urban porosity and fragmentation and offer a basis for future evaluations of residential inducement areas designated under Location Optimisation Plans.

Artificial Intelligence (AI) integration has become an essential of modern AEC workflows, yet it has failed to gain a position in waste management. This gap is particularly prominent given the urgent environmental and legal imperatives for the sector to mitigate its demolition outputs. Existing approaches to waste classification and diversion cost estimation rely on manual interpretation of project documentation, a process that is both resource-intensive and structurally incompatible with the machine-readable data environments established by Building Information Modelling (BIM). This paper presents a framework that bridges Industry Foundation Class (IFC) compliant BIM data and Large Language Model (LLM) capabilities to automate Construction and Demolition Waste (C&DW) classification and probabilistic cost optimisation. The framework utilizes IfcOpenShell to extract element geometry and material data, channeling this information into a Retrieval-Augmented Generation (RAG) pipeline. To ensure rigorous compliance during classification, a FAISS-indexed knowledge base grounds a locally deployed Llama3 model against the specific mandates of Province of Ontario, Canada regulation 102/94. Diversion cost scenarios are computed through a Bayesian cost module coupled to a multi-objective genetic algorithm (MOGA) optimiser. Th proposed approach is evaluated against a labelled dataset of 104 IFC type-and-material combinations, the RAG classifier. Performance thresholds were established a piori based on multi-class classification benchmarks and Bayesian cost model uncertainty tolerances. The framework achieved a macro-average F1 of 0.84 and overall accuracy of 88%, satisfying the minimum criteria for automated C&DW characterization under Ontario Regulation 102/94.

This case study details the integration of official large-scale open 2D and 3D geospatial data of the city of Berlin, Germany, into the Virtual Battlespace 4 (VBS4) simulator for security applications. Realistic scenery with elements specific to the target area is obtained from a digital terrain model, true-ortho mosaic, and high-resolution land use/land cover layer rasterized from OpenStreetMap vector primitives. For the central Mitte borough with its government institutions and foreign embassies, almost 20000 buildings are prepared from textured CityGML data in an automatic multi-stage process. This process involves pre-wrapping the texture images, which are referenced by the semantic 3D models using non-canonical coordinates, and the rapid creation of compact atlases to reduce the bitmap count by three orders of magnitude. To ensure that the building meshes blend seamlessly into the terrain, vertical adjustment methods are discussed, and ground extrusion is implemented to approach the model's base surfaces from below. Data import into VBS4 happens through its Geo interface for the terrain, ortho, and land cover, while the buildings are compiled into an add-on with a custom workflow that involves reprojection, collision component setup, and damage behavior configuration. During interactive convoy training in the virtual environment, a high recognition value compared to the real landscape could be attested visually. Simulation exhibited acceptable frame rates, but required considerable computing resources.

Water scarcity and increasing demand have made sustainable water management a global priority, reflected in UN SDG 6, which emphasizes water-use efficiency and reducing water scarcity. Smart Water Management (SWM) has emerged as an advanced, data-driven approach that leverages ICT and IoT systems to monitor, analyze, and optimize water use. Digital Twin (DT) technology enhances SWM by creating dynamic virtual replicas of physical systems to support predictive analytics and operational intelligence. While DTs are widely used in large-scale Water Distribution Networks, these implementations typically do not require detailed 3D modelling.

Campus-scale water systems present unique challenges due to the integration of external and interior water networks, variable building functions, and the need for detailed spatial representation. This study proposes a comprehensive DT framework for Smart Water Management at Toronto Metropolitan University. It integrates BIM, GIS, sensor data, and graph-based modelling to capture 3D interior utilities and enable real-time monitoring, hydraulic simulation, and network analysis. The framework adopts Tao et al.’s five-layer DT architecture and introduces the IFCGraph Model, which combines IFC multipatch geometry with a Neo4j knowledge graph for enhanced interoperability and topological analysis. Overall, the framework supports operational intelligence, proactive management, and scalable campus-level water system optimization.

Urban Digital Twins (UDTs) are increasingly recognized as enablers of evidence-based planning and citizen engagement. While the involvement of civil society in planning the built environment is well established, its role and motivation in advancing the clean energy transition remain largely unexplored. This paper presents the development and application of an Open Geospatial Consortium (OGC) standards-based UDT platform for the co-creation of Positive Energy Districts (PEDs), as demonstrated through the Nordbahnhof district case study in Stuttgart. The platform integrates interoperable 3D city and energy data using CityGML 2.0 with its Energy ADE 3.0 extension, both compliant with OGC standards to ensure semantic consistency and cross-domain interoperability. SimStadt energy simulation results are stored in the Energy ADE schema within PostgreSQL/3DCityDB database. These data are published through an OGC Web Feature Service (WFS), while 3D city geometries are served as 3D Tiles. In the CesiumJS web-viewer, both services are linked via GML identifiers, enabling coordinated interaction between geometry and energy data for real-time visualization of the district-scale energy balance. The platform was tested with citizens, who learned about load profiles, photovoltaic (PV) potential, and energy efficiency while acting as “district energy planners.” Their responses/willingness to adopt PV and/or modify energy-use behavior were translated into slider inputs to visualize real-time energy-balance outcomes through the platform. Results demonstrate the potential of interoperable, OGC-compliant UDTs to connect data providers, planners, and citizens in a shared decision-support environment. The architecture’s open, modular design enables wider replication, promoting scalability and long-term municipal adoption for participatory energy-transition planning.

Building Information Modeling (BIM) is increasingly mandated worldwide as part of the digital transformation of the construction industry. While widely used in design and construction, its potential for managing construction and demolition waste (C&DW) remains underexplored, despite demolition accounting for 70–90% of building-related waste and 30–40% of global solid waste. Revit models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This study develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO₂ emissions and energy intensities. A case study of York University’s Bergeron Centre illustrates the framework’s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO₂ reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.

This contribution presents a prototype Earth Observation-based simulation framework to assess how large-scale urban interventions affect Land Surface Temperature (LST). Focusing on the Metropolitan City of Milan (Northern Italy), the framework integrates thermal (Landsat 8/9) and multispectral (Sentinel-2) satellite imagery with Local Climate Zone (LCZ) maps, urban morphology and material fraction layers. Random Forest regression models are trained to predict seasonal LST patterns. A simulation module, based on raster algebra, enables scenario testing by modifying predictor layers to reflect planned urban transformations, generating corresponding LST responses. The framework is conceived for integration into Urban Digital Twin platforms to support “what-if” scenario analyses for climate-resilient urban planning and adaptation.

Urban trees play a crucial role in mitigating urban heat islands through shading and transpiration, processes directly linked to Leaf Area Index (LAI). However, estimating LAI for individual urban trees remains challenging due to their geometric and temporal heterogeneity.

This study evaluates the PATH (Path length distribution) method, a terrestrial laser scanning (TLS) based approach, to estimate LAI for three urban tree species in Strasbourg, France. The PATH method models foliage area volume density from point clouds, accounting for non-random foliage arrangements and woody structure contributions, unlike traditional optical methods.

TLS campaigns were conducted in three streets at three phenological. The sensitivity of PATH to geometric reconstruction parameters was assessed to optimize LAI estimation. Results show that envelope geometry significantly influences PAI estimates, with concave shapes (of at least 3000 facets) yielding more accurate values, while leaf angle distribution has minimal impact.

The obtained LAI estimates varied by species, reflecting species-specific crown densities. PATH-derived PAI was compared to LAI-2000 optical sensor measurements and direct LAI obtained by leaf collection. PATH estimates aligned more closely with true LAI than LAI-2000, especially during early leaf expansion, though discrepancies arose due to branch pruning and polycyclic flushing. The study highlights the importance of adapting scanning protocols and PATH parameters to species-specific morphology.

In conclusion, this work highlights the potential of TLS-based methods for providing robust PAI estimates for urban trees. Future research will link these species-specific estimates to urban microclimate benefits.

South Africa is the leading soybean producer in Africa, contributing approximately 35% of the continent’s total production. Soybean is important for national food security and agricultural sustainability–– serving as a key nitrogen-fixing crop that support soil fertility and economic growth. Whilst monitoring biochemical parameters such as leaf chlorophyll content (LCC) is essential for assessing the soya bean health, cultivar-level variability can complicate the use of remote sensing–based approaches. This study evaluates the performance of four machine-learning algorithms, XGBoost, Random Forest, Partial Least Squares Regression, and Artificial Neural Network, using unmanned Aerial Vehicle based data across 15 soybean cultivars during the early reproductive phase. Results show that model performance is strongly cultivar dependent. Tree-based models achieved the highest accuracy, with XGBoost and Random Forest reaching RMSE values as low as 2.9 µmol m⁻² for PHIP62T16R and R² values up to 0.96 for RA655R, while ANN and PLSR performed substantially worse for cultivars with more complex spectral responses, such as PAN1555R. Residual results from generalised models revealed systematic over- and under-prediction in several cultivars, indicating that models developed using pooled data are unable to fully account for cultivar-specific spectral differences. Variable-importance analyses identified red-edge, NIR, and greenness-enhancing indices as the most influential predictors of LCC, highlighting their strong sensitivity to canopy structure and chlorophyll variation. Overall, the study shows that cultivar-specific, ensemble-based modelling delivers stronger predictions of chlorophyll in soybean. Incorporating cultivar information and using stratified model calibration improves the reliability of UAV-based chlorophyll monitoring in heterogeneous soybean canopies.

The monitoring of crop traits on a landscape scale is of key interest in the context of precision farming and food production. Many studies use moderate-resolution satellite data like Sentinel-2, Landsat for crop monitoring. However, enhanced spatial resolution is improving monitoring quality significantly. In this context, commercial but expensive very high resolution (VHR) satellite data from Ikonos, Quickbird, Formosat-2, and WorldView-2 have been successfully applied for crop monitoring over the last two decades.

The focus is on the research question “Can Pléiades Neo data quantify plot-scale variation in dry biomass and N uptake?” and on developing an analysis workflow which could support precision farming on a landscape scale using VHR satellite data.

In this contribution, we propose the application of pansharpened Pléiades Neo satellite data for the monitoring of crop traits like dry biomass and N uptake - in our study for winter wheat. The very high spatial resolution of 0.3 m even allows to investigate field experiments with plot sizes of several m2 and therefore, would be suitable for crop phenotyping.

Smallholder farmers play a critical role in the growing of underutilized crops, such as sweet potato. Obtaining accurate maps of sweet potato biophysical variables is essential for farmers to assess and monitor crop health at different growth stages. Integrating radiative transfer model (RTM) data with vegetation indices (VIs) based on unmanned aerial vehicle (UAV) data, may have the potential for accurately estimating leaf chlorophyll concentration (LCC) across multiple crop varieties. Firstly, in this paper we developed and tested varying hybrid retrieval models by combining PROSAIL RTMs with broadband, narrowband and leaf-pigment VIs applied to 2-cm resolution UAV imagery, to retrieve LCC over 20 sweet potato varieties at 120 days i.e. matured growth stage. Secondly, the best hybrid retrieval model was transferred to a different site which contain similar sweet potato varieties at matured growth stage for the estimation of sweet potato LCC. Results show that the most accurate retrievals of LCC were achieved by integrating a larger database containing 11000 PROSAIL simulated reflectance samples with broadband indices, particularly the enhanced vegetation index (EVI) with coefficient of determination (R2) of 0.85, root mean squared error (RMSE) of 5.93 µg/cm2, and relative RMSE (RRMSE) of 9.87%. Furthermore, when transferred to a different site containing similar sweet potato varieties at matured growth stage, this model achieved 60% agreement with field LCC measurements and responded fairly well by capturing LCC variability. These findings have significant implications in sweet potato breeding programmes for developing new cultivars.

Remote sensing and laboratory assays can improve field-scale crop assessment and management. This exploratory pilot study analyses relationships between leaf tissue nutrients and UAV-derived normalised difference vegetation index (NDVI) using seventeen paired samples collected across a mixed crop trial. Tissue measures for nitrogen, phosphorus and potassium were standardised and entered into principal component analysis to reduce pairwise correlation and extract orthogonal nutrient axes. The first principal component explained 54.79% of variance, the second explained 34.10%, together accounting for 88.9%. Principal component scores for the first two axes were used in linear and polynomial regression models to predict NDVI. Model skill was assessed on training data and with leave-one-out cross-validation, and bootstrap resampling produced empirical confidence intervals for component loadings. Linear models built on principal components provided the most stable cross-validated performance, while polynomial expansions improved training fit but generalised poorly. These findings indicate that a low-dimensional nutrient representation can predict NDVI with reasonable stability and that combining spectral and biochemical data supports spatially explicit nutrient assessment. The study recommends expanded and stratified sampling, reflectance calibration and targeted spectral bands for follow-up studies, and external validation before wider applications.

This study integrates multispectral (UAV) and hyperspectral (ground-based) remote sensing data to estimate coffee (Coffea arabica) yield using machine learning algorithms. Forty field plots were analyzed with multispectral Mavic 3M imagery and hyperspectral Blue Wave spectroradiometer data. Spectral indices such as NDVI, NDRE, GNDVI, CIRE, and PRI were correlated with yield, revealing distinct responses across spectral domains. Neural networks achieved the best predictive performance (R = 0.93; RMSE = 7.9%), followed by SVM models (R = 0.90). The Red Edge and Green bands were most sensitive to productivity variations in multispectral data, while hyperspectral narrowband indices provided superior correlations with canopy physiological traits. The integration of both datasets highlights the complementary strengths of spatially extensive multispectral imagery and the spectral precision of hyperspectral sensing. This multiscale approach enables more accurate and operational yield estimation for perennial crops and supports the development of precision agriculture protocols for coffee production systems.

In Indoor Mobile Mapping Systems (iMMS) the trajectory estimation is implemented by the SLAM (Simultaneous Localization and Mapping) algorithm. By assuming a fixed environment surrounding the instrument, the algorithm relies on stable geometries to establish the trajectory. Drift effects represent the main source for errors and affect the trajectory estimation. These effects can be magnified in feature-deficient or degenerate environments, where the variation of geometrical elements can be minimal, as in the case of tunnels. In this context, difficult environments such as tunnels are suitable for the implementation of alternative algorithms for the trajectory estimation. Considering this kind of scenario, the contribution has the twofold objective of evaluating the results of two trajectory estimation methods, in terms of trajectory drift, with reference to an indoor SLAM-based MMS, and to establish a repeatable methodology to do so. A novel algorithm for the trajectory estimation, not just relying on geometrical SLAM algorithm, but also taking advantage of reflectance images coming from LiDAR sensors mounted on the system, is considered.

The case study is a 200 m long branch of a motor-way tunnel, with a diameter of 15 m. The test is further subdivided by computing all trajectories with different constraining strategies, first without any constraints, then considering global optimisation, loop closure and static control scans, to replicate typical realistic scenarios in tunnel mapping. The results of this work highlight how the novel reflectance-aided SLAM algorithm is beneficial in terms of drift reduction in the estimated trajectories.

This presentation addresses the challenge of range errors in point clouds of road signs captured by Mobile Mapping Systems (MMS) equipped with phase-shift laser scanners.

Under certain conditions, retroreflective materials cause range errors in point clouds. Previous studies have proposed mitigation techniques for range errors caused by sensor saturation in TOF systems, but similar studies on phase-shift systems are scarce. In addition, existing road sign detection methods assume accurate point representation, making them ineffective when sign points are displaced.

To overcome this limitation, we developed a detection method that first extracts road signs through point cloud visualization and then identifies range errors based on the standard deviation of relative distances from reference emission points.

The proposed approach was validated using 5 km of driving data collected on general roads. Results show that 32 road signs were extracted, and 26 were correctly detected as exhibiting range errors, achieving 100% agreement with manual visual assessment.

This study demonstrates the effectiveness of the proposed detection method and its potential for improving the reliability of identifying range errors of road signs on general roads.

RTK-SLAM systems integrate simultaneous localization and mapping (SLAM) with real-time kinematic (RTK) GNSS positioning, promising both relative consistency and globally referenced coordinates for efficient georeferenced surveying. A critical and underappreciated issue is that the standard evaluation metric, Absolute Trajectory Error (ATE), first fits an optimal rigid-body transformation between the estimated trajectory and reference before computing errors. This so-called SE(3) alignment absorbs global drift and systematic errors, making trajectories appear more accurate than they are in practice. We present a geodetically referenced dataset and evaluation methodology that expose this gap. A key design principle is that the RTK receiver is used solely as a system input, while ground truth is established independently via a geodetic total station. This separation is absent from all existing datasets, where GNSS typically serves as (part of) the ground truth. The dataset is collected with a handheld RTK-SLAM device, comprising two scenes. We evaluate LiDAR-inertial, visual-inertial, and LiDAR-visual-inertial RTK-SLAM systems alongside standalone RTK, reporting direct global accuracy and SE(3)-aligned relative accuracy to make the gap explicit. Results show that SE(3) alignment can underestimate absolute positioning error by up to 76\%. RTK-SLAM achieves centimeter-level absolute accuracy in open-sky conditions and maintains decimeter-level global accuracy indoors, where standalone RTK degrades to tens of meters. The dataset, calibration files, and evaluation scripts are made publicly available. The dataset, calibration files, and evaluation scripts are publicly available at https://rtk-slam-dataset.github.io/

Scene reconstruction and novel view synthesis from calibrated multi-view images still attracts a lot of attention in computer vision and graphics. However, the assumption that images are noise-free rarely holds in real-world scenarios where adverse weather conditions are inevitable. Being a part of our environment, we are particularly interested in rain as dynamic semi-transparent occlusion which imposes challenges to a complete and accurate geometry of the underlying features. More precisely, we qualitatively and quantitatively analyze the photometric image quality under rainy conditions generated by radiance field methods, namely: Neural Radiance Fields (NeRFs), 3D Gaussian Splatting (3DGS) and 2D Gaussian Splatting (2DGS) due to the different geometric representation. To assess the impact of rain to the scene reconstruction we consider raindrops and streaks captured with illumination variation as well as occlusion masks with different coverage. The evaluation is based on comparing 2D image metrics of the rendered novel views without and with masks. The experiments and results show that 3DGS achieves highest rendering fidelity in all scenarios without and with masks with SSIM of 0.724 and LPIPS of 0.291, followed by 2DGS with slightly lower scores, while NeRF exhibits lowest correspondence with the input images with SSIM of 0.584 and LPIPS of 0.384. We demonstrate the effectiveness of using masks to handle rain as transient element and radiance field methods’ ability to reliably approximate the geometry behind rain occlusions.

In recent years, many coastlines worldwide have retreated under the influence of storm surges and other extreme events, exacerbated by intensifying wave conditions in certain regions and seasons. Consequently, wave-dissipating units (e.g., tetrapods) have been widely deployed for coastal protection. In this paper, we propose a novel three-dimensional geometric method for extracting robust feature points from 3D point clouds to track tetrapod displacements and assess seawall safety. The model represents a tetrapod as four cylinders sharing a common center. By fitting this geometric model to the point cloud, we obtain parameters that allow us to derive multiple feature points—such as the intersections of conical surfaces—which can also be verified through alternative measurement techniques. These feature points serve as stable references for position comparison and displacement estimation. As this research is at an early stage, we have not yet collected field data from full-scale tetrapods. Instead, we conducted indoor experiments using a 3D depth camera (Microsoft Azure) in place of LiDAR, utilizing several high-fidelity resin tetrapod scale models (approximately 10 cm in height) as test subjects. The results demonstrate the feasibility of our method: when compared against total-station measurements, our approach yields highly accurate displacement estimates (averaging approximately 3 mm). This provides a solid foundation for the future deployment of 3D laser scanning in seawall monitoring.

As the technology of hydroacoustic sensors advances, there is a growing trend in the use of generated sonar images and point clouds in the analysis of the seabed and objects of anthropogenic origin in water bodies. In the context of cognitive and practical dimensions, obtaining data on sunken ships is of particular importance. Based on the data obtained from hydroacoustic sensors, it is possible to extract their geometric features. As a result, it is possible to develop digital repositories of wrecks, based on sonar and bathymetric data, among others, which in the future may enable the construction of integrated knowledge bases on underwater heritage. The purpose of the work was to extract the geometric features of the wreck of the Zawiszaczek located in the Puck Bay of the Baltic Sea. As part of the work, bathymetric measurements were planned, side-scan sonar and multibeam echosounder data were collected. Based on the acquired data, the geometric features of the wreck were extracted. The differences in the wreck's dimensions, as determined by sonar images obtained from different routes, did not exceed 0.25 m.

Multi-temporal remote sensing is crucial for crop monitoring, but existing mapping methods struggle with incomplete time series due to data missingness. Current models often assume consistent data, leading to performance degradation when faced with irregular or missing observations. To address this, we propose an enhanced approach combining random temporal masking with neural Ordinary Differential Equation (ODE) optimization, designed to be embedded into existing models.

Our method first employs a random temporal masking strategy during training, forcing the model to learn effective temporal dependencies from sparse, incomplete sequences, thereby boosting its adaptability to diverse missing data scenarios. Second, a time-smoothing regularization term, based on neural ODE, guides the model to learn a continuous, smooth feature trajectory from discrete observations, effectively mitigating temporal inconsistencies and abrupt fluctuations caused by missing data. We also incorporate sine-cosine positional encoding with slight perturbations for precise time representation.

We integrated our approach into the state-of-the-art TSViT model and evaluated it on the PASTIS dataset. Experiments show that while the original TSViT’s accuracy (OA and mIoU) sharply declines with increasing missing frames, our enhanced model maintains significantly better performance. At 80% missing data, our method improves OA by approximately 8% and mIoU by about 12% compared to the baseline. Qualitative results further demonstrate our model’s ability to preserve coherent, smooth spatiotemporal predictions, enhancing robustness and generalization in real-world applications.

Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced—in particular in the case of few-shot learning—failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer.

Accurate discrimination of cereal crops in heterogeneous agroecosystems requires methods that integrate both spectral and temporal information. This study proposes a compact spectral–temporal framework that combines Optimal Hyperspectral Narrowbands (OHNB) selected from EnMAP imagery using a Spectral Attention Module (SAM) with a Dynamic Time Warping (DTW)-derived phenological distance computed from Sentinel-2 EVI time series. The analysis was conducted in the Saïss region of Morocco, one of the country’s major cereal-producing areas.

SAM identified 29 physiologically meaningful narrowbands spanning the visible, red-edge, near-infrared, and shortwave-infrared regions (429–2438 nm), capturing key pigment, structural, and moisture-related vegetation properties. EVI time series were preprocessed through 10-day median compositing, linear interpolation, and Savitzky–Golay smoothing to generate stable phenological profiles. DTW quantified the temporal similarity of each field’s EVI trajectory to a cereal reference curve, producing a phenology-driven distance feature.

Three classifiers—Random Forest, SVM, and TabPFN—were evaluated under a nested standard and spatial cross-validation strategy. Using only hyperspectral bands, SVM and TabPFN achieved the highest accuracies (ROC-AUC = 0.95–0.93). Incorporating the DTW feature consistently improved performance under spatial CV, especially for RF (ROC-AUC increase: 0.89→0.91), and reduced the performance gap between validation schemes.

Overall, the fusion of SAM-selected hyperspectral bands with DTW-based phenological information enhanced spatial robustness and improved discrimination between cereal and non-cereal fields. The proposed approach offers an efficient and transferable solution for operational crop mapping in semi-arid agricultural landscapes.

Mapping banana crops in heterogeneous tropical landscapes remains challenging due to spectral similarity with surrounding vegetation, fragmented smallholder systems, and complex land-use mosaics. This study applies a deep learning approach, using a U-Net model, on high spatial resolution CBERS-4A imagery to map banana crops in Brazil’s Ribeira Valley, a subtropical region with high rainfall and heterogeneous land cover. Reference data were created through manual interpretation of satellite imagery supported by field knowledge. Representative image tiles were selected and divided into smaller patches for model training, validation, and testing. The U-Net model was trained with standard optimization techniques and evaluated using common semantic segmentation metrics. On the validation set, it achieved strong performance (accuracy 0.91, F1-score 0.84, AUC-ROC 0.96, AUC-PR 0.92). Performance was maintained or improved on the independent test set (accuracy 0.91, F1-score 0.86, AUC-ROC 0.97, AUC-PR 0.93), indicating good generalization. with high agreement between predicted and reference data. Most errors occurred at boundaries between crops and natural vegetation. Additional validation using official agricultural statistics confirmed consistency at the municipal scale. The approach demonstrates that high-resolution imagery combined with deep learning can effectively map banana crops in the region and offers a promising tool for agricultural monitoring and land-use planning in complex environments. The code, trained models, and data are publicly available at https://github.com/hnbendini/banana-unet-mapping.

This study is based on the best crop classification result generated by the proposed unsupervised Machine Learning (ML) method in Li et al., 2025a, using the spectral indices calculated by the formula with spectral bands from Sentinel-2 image products, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Normalized Difference Moisture Index (NDMI). The patterns and characteristics of these spectral indices, across arable fields with different crop types following the winter growing seasons, have not yet been analyzed in detail. This research aims to provide a comprehensive study of each input spectral index and its impact on the crop classification model. Each spectral index is analyzed across a series of crop fields, using Sentinel-2 images, carefully selected to follow the patterns of winter crop phenology, and the results of unsupervised classification for each crop type in Norfolk, UK are successfully generated and analyzed. The different growing rates between winter barley and wheat have been classified found on a monthly basis using Sentinel-2 RGB images and thus the images during the harvest time, May and June, can support crop classifications. Wild grasses or other plants on the fields led to some crop misclassification from November to March in the Sentinel-2 RGB images. Similarity between winter barley and wheat and the different sowing time among the same type of crop also led to misclassification. In future these misclassifications could be avoided through better understanding of the relation between spectral indices and crop planting cycles.

Current pest monitoring relies on labor-intensive manual scouting, often leading to preventive insecticide use, highlighting the need for automated surveillance. This study presents low-cost RGB camera sensors integrated with edge artificial intelligence (AI) for real-time aphid detection, enabling timely and targeted interventions. Using field images, we trained the YOLO11-n model and evaluated its performance under commercial farming conditions, achieving an average precision of 85 % for apterous aphids. The complex structure of lettuce, with overlapping leaves and shaded areas, limits detection accuracy, particularly for nymphal stages. Nevertheless, these results pave the way for affordable precision agriculture solutions to sustainably improve pest management.

Accurate land cover classification requires both spatial details and temporal information of remote sensing data. While publicly available satellite image time series (SITS) offer short revisit times, they suffer from limited spatial resolution. In contrast, aerial imagery provides fine-grained spatial details, but its temporal coverage is limited. Thus, combining data from those sensors is of interest as their properties are complementary w.r.t. the problem domain. However, the large gap in spatial resolution between these two sensors makes their integration challenging. Generating super-resolution-SITS (SR-SITS) before fusion can help to reduce this gap. In this work, we propose a new approach that integrates diffusion models for generating SR-SITS into a method for the joint pixel-wise classification of aerial and SITS data. Specifically, we employ a diffusion model to generate SR-SITS at an intermediate resolution from the raw SITS and aerial imagery of the same observed area. The SR-SITS are temporally encoded and fused with the aerial features using a cross attention module to produce pixel-wise classification at the geometrical resolution of aerial image. Experimental results on the existing FLAIR benchmark dataset indicate that our approach achieves state-of-the-art results, with a mean Intersection over Union score of 64.0% and an overall accuracy of 76.6%.

We present Sky-NeRF, a novel method for cloud topography estimation based on Dynamic Neural Radiance Fields. Similar to NeRF, we propose to model the 3D structure of clouds as a radiance field, encoded in the parameters of a neural representation. Our goal is to reconstruct the 3D geometry, appearance, and motion of the cloud using a stereo-video of high-resolution top of the atmosphere radiance images. In this paper, we evaluate a novel way of modeling the dynamic behavior of clouds, with the goal of extracting added-value physical information regarding the cloud such as advection speed and direction, velocity field and cloud trajectories. We investigate how to include a simple physical prior, advection, into the learning system and evaluate its impact. Our results show that Sky-NeRF is able to provide a more complete 4D reconstruction than traditional stereo-matching-based algorithms. Moreover, thanks to a physics-based interpolation, Sky-NeRF is able to generate coherent new images from unseen viewing angles, and at any time between the observed frames.

3D topographic change estimation is a fundamental task for understanding Earth surface dynamics in fields of photogrammetry and laser scanning. However, at the current state of research, it is still challenging to accurately separate and quantify various components of topographic surface changes (i.e., rigid spatial movement and non-rigid morphological deformation). In this paper, we conceptualize a surface change tensor to describe 3D surface change based on the displacement field, considering contribution of neighboring points to their center point on the surface. With this concept, we design a new method that is able to quantitatively separate rigid and non-rigid topographic change components from the mixed topographic change. Experiments on synthetic datasets demonstrate that our method is accurate and robust to quantify rigid and non-rigid surface changes, with superiority to the baseline method (M3C2). Additionally, real-world experiments on 3D point clouds collected at four epochs show the effectiveness of the proposed method for monitoring topographic dynamics and identifying geomorphological processes in complex large-scale mountain environments.

Monitoring surface change in dynamic environments is essential to preserve the integrity of human infrastructure and livelihoods from natural hazard consequences. With the advent of 4D remote sensing, near-continuous monitoring of dynamic scenes is unlocked. However, the unordered and irregular nature of point clouds, compounded by temporally variable occlusions and diverse acquisition conditions, hinders the accurate analysis of highly information-rich 4D data. This work addresses the challenge of irregular spatiotemporal sampling in time series of 3D point clouds for the case study of a dynamic sandy beach at different time scales. We explore the use of implicit neural representations (INRs) to model 4D data as continuous spatiotemporal functions that are optimised to estimate the beach topography continuously through space and time. By comparing four model variants and assessing their performance to reconstruct spatially and temporally subsampled data, we evaluate the applicability of INRs to high-frequency topographic monitoring, especially in the context of 4D change analysis. Our results show the ability to reconstruct missing epochs from time series of 3D point clouds with centimetric to decimetric accuracy at time scales ranging from seasonal to daily observations. Our findings highlight the importance of hyperparameter tuning to enable the capture of local details in complex spatiotemporal datasets. Through this, our work lays the foundation for continuous spatiotemporal representation of dynamic scenes, supporting a potentially broad range of change analysis applications.

Spatiotemporal analysis of geospatial time series data has gained increasing attention with the emergence of 4D point clouds and automatic acquisition technologies such as permanent laser scanning (PLS), time-lapse photogrammetry, and uncrewed aerial vehicle (UAV) platforms, enabling near-continuous monitoring of Earth surface dynamics for change detection and process characterization. However, facing massive data volumes through the temporal domain, current topographic data curation practices often rely on empirically determined data processing and management, which may significantly affect reusability, interoperability, and hence processing efficiency due to the absence or heterogeneous nature of metadata. The need for standardized approaches to manage time-dependent metadata has become critical as the demands for sharing data and reproducing analysis across tools and application domains increase. We propose a topographic 4D extension (topo4d) to the SpatioTemporal Asset Catalog (STAC) framework, which provides an open and extensible specification for automatic metadata curation and FAIR data management practices. This paper demonstrates how the topo4d extension facilitates the interoperability and reusability of 4D datasets and presents the corresponding metadata curation workflows applied to two real-world environmental monitoring applications.

This paper proposes the development of a multi-agent system (MAS) for Building Information Modeling (BIM) environments, where users interact with a 3D model and a chat-bot to query, validate, and analyze building elements. By leveraging conversational AI and modular agents capable of semantic understanding and geometric computation, this system allows users to retrieve data, perform quality checks, and visualize computed results directly using the BIM information. The approach supports diverse tasks, from attribute completion and filtering to volumetric calculations, thus enabling a more intelligent and accessible BIM experience for analytical purposes.

The adoption of innovative digital heritage workflows in the Architecture, Engineering, and Construction (AEC) sector faces significant challenges, particularly in integrating digital survey data with Building Information Modeling (BIM) into a unified model. This paper begins with a literature review that outlines the geometric and software-environment constraints complicating such integration and examines various proposed solutions, with particular attention to open-source tools and standard formats.

Building on this foundation, the paper introduces an innovative two-stage method: (1) segmenting, classifying, and enriching digital survey data into a BIM model; and (2) developing a web viewer that hybridizes this BIM model with the original survey data. The proposed workflow relies exclusively on open-source tools and open standards, with Industry Foundation Classes (IFC) used as the native editing format. A seamless continuity is established between the Bonsai add-on for Blender, used as a BIM authoring environment, and the web library That Open Engine, which serves as a dissemination tool enabling interactive querying of BIM data within a web browser. This library shares a common dependency on Three.js with 3DTilesRendererJS, allowing the overlay of a tiled photomesh of the asset. This integration enables the combination of an accurate geometric and visual representation with structured metadata interaction within a unified web environment. Overall, the proposed approach provides a robust and flexible framework for supporting practical applications such as dissemination, documentation, and diagnostic studies of heritage assets.

This study presents a comprehensive and semi-automated framework for generating multi-Level of Detail (LoD) 3D building models using multi-source LiDAR point clouds to support digital twin development. By integrating airborne, drone-based, mobile, and terrestrial LiDAR platforms, the framework addresses limitations of single-source datasets and enables scalable reconstruction across urban and building scales. A robust preprocessing workflow—encompassing subsampling, denoising, colorization, and two-stage registration—significantly enhances point-cloud quality and achieves seamless fusion of heterogeneous datasets with millimetre-level accuracy.

The framework supports outputs ranging from city-scale footprints (LoD0) to detailed parametric building models (LoD4), enabling applications in smart city planning, facility management, and heritage documentation. A knowledge-based segmentation layer further enables the creation of “Smart Point Clouds,” facilitating component-level querying and efficient generation of floor plans, elevations, and façade models. Real-world evaluations in downtown Toronto demonstrate high accuracy and strong computational performance, with LoD0–LoD2 models produced in minutes on a standard workstation.

By ensuring compatibility with CityGML and IFC standards, the framework enhances interoperability within digital twin ecosystems and supports integration with simulation and decision-support systems. While detailed LoD3–LoD4 modeling still requires manual refinement, the workflow establishes a foundation for future automation through AI-driven segmentation and cloud-based parallel processing. Overall, this research advances scalable 3D modeling practices and provides a practical pathway toward comprehensive, data-rich digital twins for smart cities.

With the Czech Republic's impending "BIM Act" driving the creation of a basic built environment model, the study proposes a compliant workflow for incorporating 3D models of two key vegetation feature types from the fundamental geographic vector database: "Forest ground with trees" and "Significant or lonely tree, grove." Modelling relies on nationwide datasets, the digital terrain model, the digital surface model based on image matching of aerial imagery, and supplementary aerial laser scanning data. For the forest features, the process comprised optical point cloud filtration and constrained triangulation, resulting in height-extruded forest base polygons with canopy cover tops. The 3D representation uses MultiSurface geometry, recorded as a PlantCover object in CityGML/3DCityDB, and is in line with the LoD2 standard for buildings. For solitary trees, predefined prototypes were scaled and positioned based on individual tree detection and parameters extracted from point clouds. Features were mapped to the CityGML/3DCityDB SolitaryVegetationObjects class, utilizing Implicit geometry to optimize for data volume and visualization speed. While the digital surface model, which can be easily generated from periodically acquired optical imagery, was sufficient for the forest features, aerial laser scanning data was superior in individual tree modelling. The number of extractable parameters increases with point density and is dependent on the platform used. However, the availability of such higher-density laser scanning data in Europe is limited and varies across countries and regions. The results demonstrate the generation of LoD2 compliant 3D models from nationwide datasets for both vegetation features, visually enriching the basic built environment model.

Despite the rapidly evolving and widely adopted tools in the Architecture, Engineering, Construction, and Operations (AECO) industry, Construction Supply Chain Management (CSCM) remains a fragmented practice with poor integration and interoperability between Building Information Modelling (BIM), Geographical Information Systems (GIS), and logistics systems. This research aims to bridge the gap between BIM, GIS, and logistics information by developing a unified, data-informed Digital Twins (DT) framework necessary to support multi-criteria decision-making (MCDM) in CSCM. They key characteristics of this work include: (1) a repeatable integration for heterogenous BIM-GIS environments powered by IoT networks; (2) a short-horizon predictive module optimized for construction logistics and Just-in-Time (JIT) delivery; and (3) a democratized analytics interface.

Under the background of city physical examination and assessment in territorial spatial planning, urban livability has become a focus of interest. Urban livability reflects residents' overall satisfaction with their living environment. Previous studies have been constrained by issues such as low data precision, coarse spatial scales, and limited practical applicability. To address these limitations, this study developed a refined livability evaluation framework by multi-source remote sensing data, with a primary emphasis on high-resolution domestic satellite imagery, including Gaofen (GF-1) and Ziyuan (ZY-3). Integrated with Suomi NPP night-time light data and socio-economic datasets, the research assessed four key dimensions, which were safety and resilience, residential comfort, recreation convenience, and quality and vitality in the city of Wuhan and Yibin at a detailed kilometer-grid scale. Results revealed distinct spatial patterns of urban livability of the two cities: Wuhan's central urban areas exhibited higher, more clustered livability, driven largely by quality and vitality, whereas Yibin showed a more fragmented pattern with strengths in recreation convenience but relative weaknesses in residential comfort and urban vitality. This study underscores the significant value of high-resolution, multi-source remote sensing data in enabling precise, spatially explicit livability analysis, thereby providing a scientific basis for targeted spatial planning and urban quality enhancement.

Dengue transmission in rapidly urbanising tropical cities is shaped by the combined influence of climate variability, urban morphology, and short-term surface water dynamics. This study develops a remote sensing and GIS-based framework to investigate the interaction between built-up density, waterlogging, and dengue incidence in Chennai from 2021 to 2023. Multi-source datasets, including Sentinel-2 imagery, NICFI high-resolution LULC, NDVI, and NDWI indices, Google Open Buildings footprints, IMD daily climate variables, and geocoded dengue case records, were integrated into a harmonised spatial grid for systematic analysis. Waterlogging-prone zones were delineated using a Sentinel-2 water-frequency method to capture the post-rainfall surface water accumulation rather than only persistent water bodies. Spatial clustering of dengue cases was examined using kernel density estimation, Global and Local Moran’s I, and Getis-Ord Gi*, revealing strong spatial autocorrelation and persistent hotspots in older, densely built neighbourhoods such as Kodambakkam, Adyar, Guindy, Saidapet, and Velachery, where compact built-up patterns and drainage limitations facilitate vector breeding. Peripheral areas showed weaker clustering and lower disease intensity. To assess the climatic influences, a Distributed Lag Non-linear Model (DLNM) was employed to quantify the delayed and non-linear effects of rainfall, maximum temperature, and minimum temperature on dengue incidence. Results showed notable lagged responses, with rainfall and minimum temperature exhibiting strong delayed associations aligned with mosquito development and viral incubation cycles. By integrating climatic, hydrological, and urban structural metrics, this study provides a replicable geospatial workflow for identifying micro-scale dengue-risk environments, supporting evidence-based vector-control strategies and climate-resilient urban planning in tropical cities.

Ticks are key vectors of human and animal disease, with Borrelia burgdorferi sensu stricto, the causative agent of Lyme disease, posing the greatest risk in North America. In Canada, Lyme disease cases are rising as the blacklegged tick (Ixodes scapularis) expands northward, driven by climate change, land cover shifts, and host movement. The Kingston, Frontenac, Lennox and Addington (KFL&A) region is a well-established hotspot, highlighting the importance of mechanistic models that realistically represent heterogeneous environmental drivers of transmission.

This study integrates multi-sensor Earth observation (MODIS, GEDI, Landsat) with climate, habitat, and ecological data to improve mechanistic tick phenology models. A hierarchical framework incorporates microclimate, landscape, and regional variables, enabling assessment of how sensor type, spatial resolution, and environmental gradients influence seasonal tick activity predictions. Model calibration and validation use field-collected tick and pathogen data, supplemented by citizen science observations.

By systematically linking EO to disease modeling, this approach improves the representation of environmental drivers, enhances predictive performance, and supports public health planning. The framework is transferable to other vector-borne diseases, advancing the integration of remote sensing into epidemiological forecasting at regional to national scales.

Illegal waste disposal sites pose significant ecological and public-health risks yet remain difficult to track with traditional field inspections. We propose a multi-agent detection framework that fuses textural, spectral, and contextual cues from medium-resolution satellite imagery for this work. Three specialised agents - Waste-Pile, Road, and Industry detectors - are implemented as YOLO (You Only Look Once) convolutional models that generate partial hypotheses, which are then hierarchically aggregated through rule weights learned from expert-labelled samples. The system provides an interpretable set of object relations, allowing regulators to trace how individual cues contribute to the final decision. The method was validated on an independent test area near Taromske (Dnipropetrovsk region, Ukraine) and corroborated by ground surveys. Joint aggregation raised the posterior probability of the primary target cluster from 0.27 (single-detector confidence) to 0.91, while maintaining robustness to label noise and heterogeneous sensor characteristics. Compared with conventional CNN baselines, the proposed approach delivers three key advantages: explicit explainability of outputs, transferability to 10 m spatial resolution without extensive retraining, and seamless integration of heterogeneous evidence sources. The proposed framework can serve as a cost-effective backbone for regional and national waste-monitoring systems. Future work will focus on near-real-time processing of Sentinel-2 time series, incorporation of hyperspectral and thermal methane indicators to assess remediation stages, and extension of the array of features to other anthropogenic disturbances such as open-pit mining and construction debris.

Based on traditional PS-InSAR technology, this study proposes a building elevation estimation method based on long and short baseline iteration. It utilizes long-temporal SAR images for multiple iterations to calculate building heights, which are used as prior information. Combined with the Interferometric Point Target Analysis (IPTA) method, it inverts building deformation information. The K-means clustering method is employed for PS point clustering analysis, classifying PS points with similar deformation trends and mapping them to buildings. A building safety risk assessment system is established, which comprehensively evaluates the cumulative deformation amount and deformation rate of both the building structure and its foundation. In this paper, the feasibility of the above method is verified by an example. The deformation of 9442 buildings is extracted in the study area, of which 245 buildings are in a high security risk state, and 2 buildings are in a high security risk state. Through this study, it can provide comprehensive auxiliary decision-making reference data covering macro wide-area and micro single buildings for urban construction management departments.

Accurately representing forest canopies in atmospheric models remains a major challenge due to the complex ways in which trees interact with airflow and modulate surface--atmosphere exchanges. Aerodynamic roughness is a key control variable in modelling frameworks related to air quality, meteorology, and atmospheric transport processes. In this study, we develop a physically based and spatially resolved framework to estimate aerodynamic roughness length from remote sensing observations. Specifically, using AHN (Actueel Hoogtebestand Nederland) airborne laser scanning data over a coniferous forest in Loobos, located within the Veluwe Natura 2000 region in the central Netherlands, we derive geometric roughness parameters and compare them qualitatively against eddy-covariance (EC) tower measurements at the site.

Results show that LiDAR-based roughness captures strong directional and structural variability driven by forest stand height and canopy heterogeneity, patterns that closely align with the anisotropy observed in the EC-derived displacement height and roughness length. Seasonal differences between leaf-on and leaf-off conditions further demonstrate the importance of canopy phenology in shaping aerodynamic behaviour. The spatial patterns resolved by the AHN data underscore the capacity of high-resolution laser scanning to reveal fine-scale canopy--atmosphere interactions that are entirely missed by traditional land-use--based roughness representations.

Additional opportunities remain for integrating complementary remote sensing observations (e.g., multispectral vegetation properties) to enhance the dynamical fidelity of the roughness estimates. The proposed framework provides an observation-driven pathway for parameterizing surface roughness, offering substantial potential for improving land-use representations in wind-flow and chemical transport models such as LOTOS--EUROS.

Forest canopy height (FCH) is a critical biophysical parameter that characterizes forest structure and provides fundamental information for estimating above-ground biomass and carbon stocks. The Global Ecosystem Dynamics Investigation (GEDI) Level 2A (L2A) product offers accurate canopy height observations; however, its point-based nature constrains spatial continuity in FCH mapping. This study integrates the multimodal remote sensing datasets for continuous FCH mapping in Tanzania’s West Usambara (WUSA) forest, recognized globally for its rich biodiversity and ecological significance. Hence, remote sensing data, including Sentinel-1 polarizations (VV and VH), Sentinel-2 spectral bands and vegetation indices, and the SRTM digital elevation model (DEM), were integrated and matched with GEDI canopy height data used as reference for FCH modelling. The optimal feature set was derived by evaluating the performance of several feature selection and extraction methods, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Recursive Feature Elimination (RFE), Sequential Feature Selection (SFS), and the Selected K-Best approach using F-value and mutual information scoring functions. The feature set derived from RFE, comprising ten features from all data sources, demonstrated the highest accuracy and reliability in FCH modelling. Subsequently, four machine learning algorithms, including Random Forest (RF), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Ordinary Least Squares (OLS), were evaluated for FCH modelling. Accordingly, RF achieved higher R² than GBR, SVR, and OLS, with differences of 0.9%, 8.7%, and 16.4%, respectively. Therefore, the RF model, as the most reliable model, was employed for FCH mapping across the WUSA forest.

Canopy height models (CHMs) are raster maps representing normalized tree canopy height above ground and are often used as co-products for estimating carbon storage, forest degradation, and biodiversity at regional to global scales. While airborne LiDAR delivers the most accurate canopy height (CH) measurements, its high cost and limited temporal coverage motivate the use of spaceborne (multispectral) imagery combined with machine learning.

In this study, we compare two distinct deep-learning approaches for continental-scale CHM estimation from 3 m PlanetScope imagery: (1) a CNN-based regression model (DeepLabv3+), and (2) a monocular depth-estimation model (Depth Anything V2) based on a foundation model. We train/fine-tune both models on a curated dataset of 16,973 pairs of airborne point cloud-derived CHMs and PlanetScope imagery of Australia using a stratified sampling scheme to ensure balanced representation of vegetation structural classes. We then evaluate their generalizability on independent validation sets across Australia, across different heights, and under limited-data scenarios.

Through extensive quantitative and qualitative analysis, we show that the DeepLab-based regression model outperforms Depth Anything across all evaluation metrics, partly because it can incorporate additional spectral channels. DeepLab also learns more effectively from less data. On our dataset, the conventional CNN-based regression model performs better than the fine-tuned foundation model.

To address the critical challenge of regional transferability for ALS-based above-ground biomass (AGB) models, we developed and applied a rigorous leave-one-region-out cross-validation (LORO-CV) framework. This protocol integrates a <1 SE “near-zero” bias filter to ensure models are not just accurate, but statistically free of regional bias.

With this framework, we compared two distinct modeling methods: a data-driven Best-Subset Selection (BSS) method and a Functional Regression (FR) method. The analysis was based on 163 field plots and co-located multispectral Titan ALS data from four regions in the Taiga Plains ecozone, Canada.

The BSS method identified a transferable linear model using height skewness, p95, and an intensity-weighted metric, which achieved 19.3% LORO-CV %RMSE and 2.0% mean absolute bias. Crucially, it passed our <1 SE bias screen in all regions. The FR model, relying only on height, achieved 22.4% LORO-CV %RMSE (4.1% bias) but failed the bias screen in two regions.

Our findings demonstrate that a systematic, bias-controlled data-driven method is effective for producing regionally transferable models. The results highlight the critical importance of ALS intensity metrics for this success, while also showing that the data-driven method currently surpasses the functional approach.

The role of forests in carbon sequestration and regulation is important to understand, given the alarming rate of global warming caused by greenhouse gases. Understanding the structural characteristics of trees can help assess the potential of forests for carbon storage. Light Detection and Ranging (LiDAR) has emerged as a powerful remote sensing tool that is capable of providing detailed three-dimensional information of the forest. The increasing availability of aerial LiDAR data has provided opportunities to estimate the forest biomass over a larger extent. This study utilizes the available LiDAR data from the provincial repository of geospatial data to estimate the diameter at breast height (DBH), which is a key parameter in existing biomass allometric models. LiDAR-derived tree metrics were integrated with the optical images to further differentiate the forest type to assess how it influences the aboveground biomass estimates in a heterogeneous mixed-wood forest. This research contributes to improving our understanding of LiDAR's potential for estimating DBH, an area that has not been explored much. It also demonstrates how existing global biomass allometric equations can be utilized in combination with remote sensing technology to provide a pathway to a larger extent and an efficient method of biomass estimation across diverse ecosystems.

Communication towers equipped with cameras are widely distributed across rural landscapes but remain largely unused for scientific observation. This presentation introduces an AI-driven framework that transforms such existing infrastructure into a high-frequency, real-time agricultural monitoring system, complementing traditional satellite and UAV remote sensing.

The proposed system resolves three fundamental challenges that hinder tower-based sensing: (1) precise georeferencing of highly oblique imagery through a quaternion-based spatial transformation; (2) automated delineation of cultivated parcels via a GIS-guided, iterative segmentation process integrating the Segment Anything Model (SAM); and (3) intelligent recognition of crop types, growth stages, and farming activities using a multimodal large language model that fuses time-series imagery with contextual field data.

Validated through deployments in varied agricultural regions of China, the framework demonstrates stable operation and parcel-level accuracy for continuous monitoring within 1–2 km of each tower. The results indicate a practical pathway toward scalable, cost‑efficient, and autonomous agricultural information acquisition at high spatio‑temporal resolution.

Estimating camera poses jointly from aerial and ground imagery remains difficult because large viewpoint changes reduce overlap, alter appearance, and weaken the geometric assumptions relied on by both classical photogrammetry and recent AI-based reconstruction models. This paper presents a controlled comparison between a classic photogrammetric approach represented by COLMAP and a cross-view fine-tuned end-to-end model based on Dust3R. Tests are carried out on a London building scene containing 10 aerial and 29 ground images. Fine-tuned Dust3R reconstructs the full image set, whereas COLMAP successfully registers 24 ground-level images. Because both reconstructions are defined only up to an unknown similarity transform and no ground-truth poses are available, we evaluate the shared subset through 7-DoF similarity transformation analysis rather than direct metric pose errors. After transformation, the translation RMSE of the shared camera centres is 10.0\% of the reconstructed scene diagonal in the fine-tuned Dust3R coordinate frame. We further compare pairwise geometric support using a unified fundamental-matrix RANSAC evaluation over 406 image pairs. The AI-based pipeline achieves substantially higher inlier ratios than photogrammetric pipeline under the same verification settings, indicating more successful cross-view orientation. The study contributes a clearer evaluation protocol for mixed aerial-ground pose estimation without ground truth, together with an empirical analysis of robustness, alignment behaviour, and current limitations of both pipelines.

We propose a generic method for epipolar resampling that is not tied to a specific camera model. We demonstrate the effectiveness of the approach on a central perspective, pushbroom and pushbroom panoramic camera models. We also devise an \textit{epipolarability index} that measures the suitability of an image pair for epipolar rectification, and provide a formal derivation of the ambiguity bound to epipolar resampling. An open-source implementation of the algorithm is available at github.com/micmacIGN/micmac

Thermal infrared (TIR) imaging provides surface temperature of the objects and reveals heat-transfer patterns of buildings, which supports applications such as insulation inspection, energy leakage, and thermal bridge detection. However, the TIR image dataset with reliable annotations for deep learning remains scarce, as the labeling process is time-consuming and tedious, and particularly challenging due to the low-texture and blurred features of TIR images. To address this challenge, we propose ThermalAssist, a geometry and gradient-aware framework designed to assist thermal anomaly labeling in TIR imagery. By combining sparse manual annotations with dense correspondence via flow-based propagation, the framework efficiently transfers labels across image sequences while preserving semantic consistency and boundary integrity. Experiments on the TBBR dataset demonstrate that ThermalAssist can transfer labels between images, achieving up to 21% higher F1-score and 35% higher precision compared to state-of-the-art tracking-based baselines. It also helps identify missing annotations and boundary inconsistencies for quality checks. This work provides a foundational tool for quality-assured thermal annotation pipelines and represents a key step toward more scalable, reliable, and intelligent labeling of thermal imagery.

Accurate image matching is essential for the precise orientation of airborne imagery, yet modern feature matchers are rarely evaluated on real aerial data with great temporal, seasonal, and radiometric changes. For this study, we introduce the AerialRefMatch dataset, which comprises 51 challenging aerial images and corresponding true-ortho reference data. We benchmark classical and deep learning–based matching algorithms on AerialRefMatch, considering two scenarios: matching original images and matching approx-orthorectified images generated using GNSS/IMU orientations. For each method, image-based ground control points are derived and used for single-image pose estimation; accuracy is assessed via independent checkpoints. Results show that directly matching on original images is very difficult: fewer than 14\% of images can be oriented with pixel-level accuracy. When approx-orthorectification is used, performance improves substantially. JamMa, SIFT, and SuperPoint+LightGlue achieve pixel-level accuracy for up to 30\% of images, with JamMa being most robust on difficult cases and SIFT-based variants being more precise on the easier ones. Deep detector-free models such as ELoFTR and RoMa are less accurate but more robust to the original images than other models. Overall, state-of-the-art deep learning-based matchers still struggle with large rotations, scale differences, and semantic differences, and strongly benefit from prior image orientation knowledge and lack sub-pixel precision.

Deep learning-based image matching faces a critical challenge when deployed on computationally constrained embedded aerial devices. Transformer-based architectures, particularly the scaled dot-product attention mechanism, incur high computational costs that limit inference speed for real-time applications. To address this bottleneck, we propose FastGlue, a sparse feature matching algorithm that adapts the LightGlue architecture through two targeted modifications: replacing the scaled dot-product attention with a ReLU-based linear attention module, and reducing the depth of the graph neural network. These changes reduce computational complexity while maintaining competitive matching performance. Evaluations on HPatches and MegaDepth-1500 benchmarks show that FastGlue achieves accuracy comparable to LightGlue while improving inference speed—from 20.05 ms to 17.05 ms on GPU, and from 840.45 ms to 665.85 ms on an RK3588 embedded CPU. Our work demonstrates that targeted architectural simplifications can yield meaningful efficiency gains for deep learning-based feature matching on resource-constrained platforms.

We present SCOP, an open-source and educational framework for generic photogrammetric bundle adjustment built in Python and powered by JAX automatic differentiation. SCOP removes the need for manual Jacobian derivation by expressing all projection models as pure mathematical functions with automatically computed exact derivatives.

The framework supports multiple camera geometries (pinhole, fisheye, equirectangular) and optimization methods (Gauss-Newton, Gauss-Newton-Armijo, Levenberg-Marquardt, Gradient Descent). Its modular architecture, separating cameras, images, and observations, allows easy extension to new sensors and constraint types, including GNSS positions, ground control points, and geodetic observations.

A hybrid computation pipeline combines JAX for differentiation with a Rust backend for sparse Schur complement elimination, achieving ~0.5 s per iteration on a real-world dataset with 79k unknowns and 181k observations. Following classical least-squares photogrammetry, SCOP provides rigorous uncertainty estimation through covariance matrices, normalized residuals, and reliability indices. With synthetic data tools and interactive 3D visualization, it enables transparent teaching and reproducible research.

Local feature matching in complex scenes is hindered by semantic ambiguity, where detectors often latch onto transient or repetitive patterns. We present TriCo-Net, which learns semantically aware and discriminative local features by enforcing a Triple Consistency (TriCo) principle across implicit semantics, scale, and spatial context. During training, an Implicit Semantic Strategy (ISS) distills cues from a segmentation teacher to modulate keypoint reliability and descriptor learning, while introducing no overhead at inference. A Scale-wise Semantic Harmonizer (SSH) aligns and fuses feature-pyramid levels to ensure cross-scale coherence, and a Global Context Propagator (GCP) broadcasts scene-level dependencies to resolve local ambiguities. On Aachen Day–Night v1.1, TriCo-Net achieves strong and consistent gains in visual localization, particularly under night conditions, and exhibits robustness to blur, noise, and large homographies. Ablations show complementary benefits from ISS, SSH, and GCP, with ISS contributing most at tight thresholds and at night. TriCo-Net narrows the day–night performance gap while maintaining mid-range throughput, offering a practical trade-off between robustness and efficiency.

The development of high-performance, affordable UAVs has transformed vegetation monitoring, enabling observation of forest canopies at an unprecedented level of detail. UAV-derived datasets now provide high-fidelity structural and physiological information at the individual tree level across entire forest stands, offering novel insights into forest dynamics. In the context of increasing tree mortality, such data are becoming essential for understanding forest resilience and adaptation. However, exploiting this data requires effective individual tree crown segmentation algorithms (ITCS) at the forest scale, capable of tackling large-scale data and variability introduced by the environment. In this paper, we developed a new workflow designed to process UAV hyperspectral imagery at the forest scale, enabling automated ITCS and analysis. Our pipeline integrates hyperspectral-to-RGB conversion, ITCS, and centroid-based mask fusion. To assess the performance of our pipeline, we evaluated the model on two replicated white spruce common gardens in Canada, each comprising approximately 6,000 trees of similar age and structure. The experiments rely on a large multi-temporal dataset of hyperspectral imagery acquired during 60 UAV missions between 2022 and 2024, allowing us to evaluate the robustness of the proposed pipeline across a wide range of seasonal and acquisition conditions. Results show that the proposed pipeline achieves a mean segmentation performance of 0.536 mAP (0.885 mAP50) on the annotated dataset. At the forest scale, the system demonstrates strong detection capability with F1-scores of 0.948 at the Pintendre site and 0.863 at the Pickering site, successfully detecting most trees while maintaining stable performance across varying environmental conditions.

Individual tree detection and crown delineation (ITDCD) in dehesa landscapes is complicated by geometric distortions from steep terrain, varying tree densities, and the partly multi-crown 'broccoli-like' structure of holm and cork oaks. This study evaluates the usability of a modified StarDist deep learning model, which has recently shown effectiveness for ITDCD in Canadian forests. Moreover, this study develops a workflow transforming the original StarDist, designed for microscopy images, into an ITDCD solution, taking the georeferencing of geospatial data into account. The tile-wise organized ground truth dataset is created with the pretrained Tree Segmentation model available in the ArcGIS Living Atlas, combined with manual revision. Several augmentation methods are applied, resulting in 960 images, which are split into 85 % for training and 15 % for validation. Following the approach of the Canadian forest study, the StarDist implementation is modified by introducing a constraint to the probability loss function. Rather than computing loss across all pixels, the modified loss function considers only pixels explicitly annotated as objects, while background pixels are excluded. An additional dataset of 1,200 trees serves as ground truth for testing the prediction across the entire study area. Using an Intersection over Union of 0.5, this test demonstrates good performance (Accuracy: 87.50 %; F1-score: 0.85). The accuracy varies with tree density: in areas with sparse tree cover, nearly all tree crowns are detected; in moderately dense areas, a number of tree crowns are missed; whereas in very dense tree layers, the frequency of missed detections increases.

Individual tree crowns detection and delineation from airborne LiDAR data is essential for forest inventory, carbon stock estimation, and ecosystem monitoring. In mixed-wood forests, however, this task remains difficult due to high stand density, multi-layered canopy structure, and the wide variation in crown size and shape across coniferous and deciduous species.

This study addresses two core limitations of existing deep learning methods for individual tree crown delineation. Standard instance segmentation models rely on blind anchor-based proposals that frequently miss small understorey trees in dense canopies, and their pixel-based mask representations struggle to accurately capture crown boundaries for small or irregular crowns.

We propose a multi-task learning framework that jointly trains a structure-aware treetop detection head and a crown segmentation head on a shared backbone network. The treetop detection head generates spatially precise crown seeds guided by canopy height and allometric relationships, replacing blind anchor proposals with data-driven initialisation. Two segmentation strategies are evaluated within this framework: a Mask R-CNN pixel-based approach and a StarDist contour-based approach.

Experiments are conducted on a high-density airborne LiDAR dataset acquired over a mixed-wood forest in Ontario, Canada, comprising 4,417 manually delineated reference crowns. Results demonstrate improved detection completeness for small crowns and more accurate boundary delineation for overlapping larger crowns compared to single-task baselines.

This study examines the use of multi-temporal UAV hyperspectral and LiDAR data to identify tree species in a mixed deciduous forest in southern Ontario, Canada. Weekly UAV flights were conducted from summer through spring to capture structural and spectral changes associated with leaf development, senescence, and leaf drop. Field measurements were collected to provide species labels and biometric information for individual trees.

LiDAR data are processed to delineate individual tree crowns and to derive structural metrics such as crown height, width, density, and vertical canopy profile. Hyperspectral imagery, consisting of more than 300 bands, is co-registered with the LiDAR-derived crowns to extract spectral signatures and compute vegetation indices. These data support the development of a spectral library for the main species in the study area.

The multi-temporal dataset allows evaluation of how phenological changes influence separability among species. Early leaf loss in autumn and differences in budburst timing in spring are expected to produce temporary structural and spectral contrasts that aid classification. Machine learning models, including random forest and neural networks, are applied to assess the contribution of structural, spectral, and seasonal features to species discrimination.

Accurate 3D reconstruction and instance segmentation of soybean seedlings are crucial for early phenotyping and precision agriculture. This study presents a UAV-based sparse-view 3D reconstruction and plant-level segmentation framework that integrates 3D Gaussian Splatting (3DGS) with Mobile-SAM, enabling efficient and high-fidelity modeling under routine field conditions. Traditional LiDAR and MVS approaches, while detailed, are constrained by cost, acquisition density, and computational complexity. By contrast, 3DGS offers explicit Gaussian primitives for fast rendering and direct geometric access but often fails under sparse-view UAV imagery due to weak multi-view constraints and repetitive canopy structures.

To overcome these limitations, the proposed method introduces a mask–geometry co-optimization mechanism: YOLO-generated bounding-box prompts guide Mobile-SAM to produce accurate single-view plant masks, which serve as semantic priors to associate 2D observations with 3D Gaussian primitives. Iterative refinement aligns rendered and observed masks, ensuring spatial consistency and coherent 3D plant boundaries.

Field experiments on a soybean plot demonstrated the method’s effectiveness, achieving high reconstruction quality and visually precise seedling segmentation. The resulting 3D models capture fine structural details and distinct plant instances even under sparse-view UAV data.

This work highlights the potential of combining explicit geometric modeling and lightweight semantic segmentation to achieve robust, scalable, and field-deployable 3D crop reconstruction, offering a promising pathway for high-throughput plant phenotyping and yield estimation in real-world agricultural applications.

In this study, we propose an upscaling approach based on 8-band PlanetScope SuperDove imagery (Coastal Blue, Blue, Green I, Green, Yellow, Red, Red Edge, NIR) combined with UAV data. We employed an evidential Dirichlet neural network to estimate the fractional cover of 13 herbaceous and shrub species typical of Mediterranean coastal dunes, previously mapped at 3 cm using a traditional Random Forest classifier trained on UAV multispectral samples. The overall goal is to enable large-scale mapping of coastal vegetation using high-resolution satellite imagery.

This work addresses the emerging challenge of achieving proactive spatial cognition for embodied and spatial AI systems operating in dynamic real-world environments. Conventional mapping and reasoning approaches are largely passive and task-dependent, limiting their ability to build persistent understanding beyond immediate goals. We introduce the Proactive Cognitive Map (PCM), a unified framework that enables agents to autonomously construct, verify, and refine their spatial knowledge through continual perception, self-questioning, and mental simulation. PCM integrates a grid-based perceptual map with a semantic, object-centric memory, forming an explicit and interpretable representation of the environment. A self-questioning module identifies uncertain or ambiguous regions and generates targeted queries, while a simulation module emulates human imagination to perform counterfactual reasoning and lightweight geometric self-verification across time and viewpoints.

We evaluate PCM across episodic-memory embodied QA tasks and the long-horizon, multi-task benchmarks, GOAT-Bench, covering episodic reasoning, continual understanding, and cross-task generalization. Results show that PCM’s self-driven graph construction and proactive refinement outperform goal-specific exploration methods.

By transforming mapping from static perception into a continual cognitive process of questioning, imagining, and verifying, this study provides a step toward lifelong, interpretable, and self-improving spatial intelligence.

As a spatial digital foundation for digital twins and smart cities, the timeliness and accuracy of realistic 3D models are of critical importance. Intelligent and automated data acquisition and update workflows form the core infrastructure that sustains this digital foundation. Current modeling techniques relying on a single data source face inherent limitations: UAV(Unmanned aerial vehicle)-based oblique photogrammetry struggles to capture lower facade details, often leading to geometric distortions and blurred textures, while conventional terrestrial surveying methods suffer from low efficiency and limited automation as well as intelligence. Moreover, the substantial viewpoint differences between aerial and ground data hinder effective fusion. However, recent technological advances in 3D Gaussian Splatting (3DGS), large vision model, multi-sensor SLAM and robotic systems, open up new opportunities to significantly improve the fidelity, efficiency, completeness and automation of 3D reconstruction through the cooperation of UGVs and UAVs.To address the current challenges from 3D reconstruction, this study proposes a novel framework which seamlessly integrates autonomous unmanned systems, state-of-the-art large visual models, multi-sensor SLAM (simultaneous localization and mapping) and cutting-edge 3D Gaussian rendering technology. The framework realizes an integrated workflow for automatic updating building facade and high-fidelity 3D GS rendering using air to ground fusion algorithms with autonomous systems. The primary focus is to advance the automation and intelligence of building 3D reconstruction, thereby enabling efficient updates of urban 3D models.

High-precision digital terrain models (DTMs) are important for Mars explorations and research. However, traditional terrain reconstruction methods suffer from limitations in coverage and resolution. To enhance the model's ability to recover fine-grained topography, we present a diffusion-based monocular terrain reconstruction method, which progressively recovers Martian terrains from single-view high-resolution optical images. We employed a multi-scale U-Net denoising network with attention mechanisms and introduced an additional end-to-end depth constraint. To improve terrain reconstruction efficiency, we implemented a diffusion model in the latent space and adopted a skipping sampling mechanism. We employed the proposed method to reconstruct terrain in different regions. Experimental results demonstrate that the reconstructed terrain achieves an accuracy of 2 m. Furthermore, compared to photogrammetric terrain, the shaded relief generated by our method exhibits greater similarity to the input imagery.

GESM is a Gaussian-mixture sonar mapping pipeline that converts 2D imaging sonar into a continuous 3D probabilistic map for navigation. We estimate posterior occupancy with Gamma-CFAR, cluster occupied and free space along beams, encode them with weighted EM/MPPCA and moment-matched Gaussians, and incrementally merge local mixtures into a globally consistent map. Loop closure is handled by in-place edits of mixture parameters. On simulation and pool/harbour data, GESM yields dense, navigation-ready structure and free water while reducing map memory by ~99% compared with a comparable voxel grid.

This paper investigates how the provenance and resolution of geospatial data used to construct mesh maps affect the accuracy and robustness of mesh-based visual localisation. Mesh-based approaches offer significant advantages over traditional pipelines reliant on Structure from Motion (SfM) models, including the ability to scale to city-sized scenes---by leveraging large-scale data sources such as national mapping databases--- and on-demand generation of arbitrary synthetic views. While prior work has focused on algorithmic improvements to mesh-based localisation, none has systematically analysed how different input data affect localisation outcomes. In this work, we evaluate three meshes---derived from aerial oblique imagery, combined aerial and ground mobile mapping data, and close-range ground imagery---across the egenioussBench Extended and House of Science query sets and four image matchers. We show that mesh quality is the dominant factor governing localisation performance. In the House of Science experiments, aerial meshes lack the resolution required to resolve façade detail, causing near-total localisation failure regardless of matcher. In the egenioussBench Extended experiments, augmenting an aerial mesh with ground data yields consistent but less dramatic improvements. We further introduce the Perceptual Detail Score (PDS), a viewing-condition-aware metric that proves to be a strong predictor of downstream pose accuracy across all experimental configurations.

The accurate recognition of segment joints serves as a critical step for capturing joint anomaly information, evaluating segment assembly quality, diagnosing structural health status, and determining the loosening of connecting bolts. It holds significant importance for the operation and maintenance of shield tunnels. However, existing studies on joint recognition based on Rail-borne Mobile Laser Scanning (RMLS) suffers from insufficient comprehensiveness in feature representation, leading to notably poor accuracy and robustness under complex scenarios such as noise interference, data loss due to object occlusion, and uneven point cloud density. To address this issue, this study proposes a shield tunnel segment joint recognition method based on the Joint Composite

Feature Index (JCFI). The proposed method first employs a cross-sectional ellipse fitting approach to filter out obvious non-lining points. Subsequently, a composite index JCFI, which integrates curvature, left-right density ratio, and relative depth, is designed to

quantitatively characterize the feature differences of segment joints. Finally, based on the constructed JCFI indicator, the recognition of circumferential and longitudinal joints is sequentially achieved. Validation tests using RMLS point cloud data from the Guangzhou Metro Line 8 tunnel demonstrate that the proposed method, by constructing the JCFI that comprehensively characterizes joint features, effectively handles complex scenarios including noise interference, joint missing, and uneven point cloud density. The

joint recognition achieves a recall rate of 90.14%, a precision rate of 99.04%, and an IoU of 89.36%, providing a reliable technical solution for the accurate identification of shield tunnel segment joints.

Severe convective rainfall, triggered by multi-scale atmospheric interactions, poses a critical forecasting challenge in coastal cities like Shanghai, where monsoon, topography, and sea-land breeze amplify extremes. Conventional methods, constrained by scale separation and model biases, struggle to predict convection. This study develops the Synergistic Framework for Convective Rainfall Forecasting (SSF-CRF) by integrating three modules: (1) Adaptive S/X-band radar remote sensing, dynamically capturing mesoscale convective structures; (2) Gated Vertical Information Propagation (GVIP) network, machine learning on vertical energy propagation to capture convection; (3) Precipitation Ordinal Distribution Autoencoder (PODA), correcting numerical weather prediction (NWP) biases with ordinal precipitation classification. Verification against Radar data and European Centre for Medium-Range Weather Forecasts (ECMWF) model indicates that SSF-CRF improves heavy rainfall (≥50 mm/h) Critical Success Index (CSI) by 33% versus operational forecasts. It offers a potential solution for convective forecasting in climate-vulnerable coastal regions, advancing remote sensing-driven atmospheric applications.

Monitoring atmospheric water vapour is essential for weather forecasting and climate studies. GNSS networks can retrieve Precipitable Water Vapour (PWV) continuously at each station location, but the accuracy depends on the quality of the satellite orbit and clock corrections used in the processing. This study evaluates PWV retrieval from 478 stations of the French Centipede low-cost GNSS network using four levels of correction products with decreasing latency: GFZ Final ($\sim$2 weeks), Rapid ($\sim$1 day), Ultra-rapid (3--9 hours), and broadcast ephemerides (real-time). Validation against ERA5 reanalysis shows that the Final and Rapid products achieve similar performance (RMSE $\approx$ 2~mm, $r^2$ = 0.84), confirming that near-real-time processing introduces no significant accuracy loss. Ultra-rapid products remain usable (RMSE = 3.4~mm), while broadcast ephemerides show larger errors (RMSE = 5.8~mm) but still capture the spatial moisture pattern. In addition, a real-time experiment using the freely available Galileo High Accuracy Service (HAS) demonstrates that stable tropospheric estimates (ZTD $\pm$ 1.4~mm, PWV $\pm$ 0.2~mm) can be obtained in real time, even before the positioning solution has fully converged. These results suggest that combining the spatial density of low-cost networks with real-time HAS corrections could enable high-resolution PWV monitoring that is not achievable with existing systems.

Fengyun-3G, launched in 2023, carries Ku- Ka dual-frequency precipitation measurement radar (PMR) providing new opportunities for monitoring the fine three-dimensional structure of typhoon precipitation over the ocean. This study first validate the FY-3GPMR data by using the ground-based data, then utilizes PMR to analyze the precipitation during the rapid intensification phase of Super Typhoon Yagi in the year of 2024. The analysis reveals the horizontal and vertical distribution characteristics of precipitation during Yagi's RI phase based on the FY-3G PMR data, and discusses the associated dynamical-microphysical coupling mechanism. Overall, FY-3G PMR offers critical insights for understanding cloud and precipitation process involved in the RI.

Atmospheric water vapor plays a fundamental role in regional climate regulation and precipitation formation, yet its vertical structure and spatiotemporal evolution over mainland China remain insufficiently understood due to complex terrain and diverse climatic conditions. Using Fengyun-4A layered precipitable water (LPW) products from 2020 to 2023, this study provides a comprehensive assessment of the vertical distribution, spatiotemporal variability, and key environmental drivers of water vapor across China. Results show pronounced spatial gradients and seasonal contrasts: total precipitable water (TPW) exhibits a slight overall decline, primarily driven by reductions in low layer; spatially, TPW is highest in the southeast and lowest in the northwest; seasonally, water vapor peaks in summer and reaches its minimum in winter, with spring and autumn representing monsoon-transition phases. Vertically, approximately 75% of atmospheric water vapor is concentrated within the lowest 4 km, with the middle layer contributing most to regional differences, while high layer remains relatively uniform and minimally influenced by terrain. Environmental correlations indicate that TPW is positively associated with 2m temperature, relative humidity, surface pressure, total cloud cover, and precipitation, but negatively associated with DEM and evaporation. Layer-dependent responses indicate that the lower layer is strongly influenced by surface processes, the middle layer by both surface moisture transport and large-scale circulation, and the high layer primarily by thermodynamic structure and synoptic background. These findings, derived from high-resolution satellite observations, enhance understanding of atmospheric water vapor stratification and its controlling mechanisms, providing essential support for water vapor transport diagnosis, precipitation evolution, and operational forecasting improvement.

In recent decades, global climate change has triggered a rise in extreme environmental phenomena, including prolonged droughts, intensified precipitation events, and shifts in tidal patterns. This study focuses on the application of the observations from Global Navigation Satellite System (GNSS) signals for monitoring and classifying climatic conditions, with particular emphasis on drought.

Using daily vertical displacement data from a GNSS station in California (2005–2023), we developed a robust analysis framework. It includes data cleaning (removing outliers, filling gaps, detecting offsets, and modeling noise), trend and seasonal pattern extraction through Singular Spectrum Analysis (SSA), feature generation (like amplitude, energy, and dominant frequency), labeling

based on the Standardized Precipitation-Evapotranspiration Index (SPEI), and classification using a hybrid CNN-Transformer model. The results demonstrate the model’s capability to accurately detect drought periods (SPEI > -1) characterized by diminished amplitudes in seasonal components and heightened noisy fluctuations, as well as wet periods (SPEI < 1) marked by elevated energy in semi-annual signals. The model was evaluated with an overall accuracy of 83.3 percent, an F1-score of 0.90 for the drought class, and successful application to future data (2024–2029). This approach, independent of traditional meteorological data, underscores the potential of GNSS as a geodetic tool for environmental monitoring, albeit with limitations such as reliance on single stations and the need for supplementary datasets. The methodology holds promise for enhancing early warning systems and climate models.

Carbon dioxide (CO₂) and methane emissions (CH₄) are primary greenhouse gases whose rising atmospheric levels intensify global climate change. Wetlands, despite covering only 5–8% of Earth’s land area, contribute nearly 30% of global methane emission while storing up to 30% of global soil organic carbon. This makes wetlands both sinks and sources of greenhouse gases, though their seasonal CO₂ and CH₄ dynamics in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) remain poorly understood.

Ground-based instruments offer high accuracy but limited spatial coverage, whereas satellite missions, such as Sentinel-5P/TROPOMI for XCH₄ and OCO-2 for XCO₂, enable wide-area monitoring. This study investigates the seasonal dynamics of CH₄ and CO₂ across different wetland ecosystems in the GBA using satellite observations and ERA5-Land climate variables. Seasonal means were computed in Google Earth Engine for Winter, Spring, Summer, and Autumn from 2019 to 2025.

Results show a consistent rise in atmospheric CH₄ from 1856 ppb (2019) to 1939 ppb (2025), with the highest levels in Autumn and Winter. CO₂ increased from 404 ppm to 424 ppm, peaking in Winter and Spring. Non-wetland regions and mangroves emerged as the primary contributors to greenhouse gas accumulation, while salt marshes and other wetlands showed lower values.

Pearson correlation analysis indicated strong influence of temperature, dew point, and precipitation on CO₂, while CH₄ showed variable sensitivity to rainfall and wind. Findings emphasize the impact of land-cover type and climate in shaping seasonal greenhouse gas dynamics, supporting SDG 13 and SDG 15, and necessitating hyperspectral data integration for climate policies.

Urban air quality remains a critical concern, as NO₂ emissions from transport and industrial activities frequently exceed healthy limits in major cities. Urban vegetation can help reduce pollution by enhancing natural filtration and cooling, while higher land surface temperatures (LST) tend to intensify pollutant accumulation. Using satellite-based remote sensing, this study investigates how vegetation health (NDVI) and surface temperature influence NO₂ levels in two distinct urban environments: Blackburn/Arlington Road in England and District No. 3 in Tehran, Iran, across pre-, during-, and post-COVID-19 lockdown periods. Both cities experienced notable environmental improvements in 2020: NDVI increased from approximately 0.45–0.48 to around 0.54–0.61, while NO₂ dropped significantly from about 0.46–0.50 to roughly 0.13–0.35. LST also declined from pre-lockdown values near 0.46–0.48 to as low as 0.12–0.38. During the lockdown, vegetation levels showed a clear negative relationship with NO₂ concentrations, and pollution trends displayed a strong positive association with higher temperatures, highlighting the linked benefits of greener and cooler environments. However, as human activities resumed after 2021, these relationships became inconsistent or weakened, with occasional shifts in direction depending on seasonal conditions and external drivers such as traffic recovery and industrial intensity. Overall, the results reinforce that increasing vegetation coverage and mitigating urban heating can meaningfully reduce NO₂ levels. By revealing how urban form, vegetation dynamics, and thermal conditions collectively shape pollution patterns, this research provides insights for city planners, environmental managers, and public health authorities working to design more sustainable and healthier urban environments.

An Open Source Framework for Routing and Event Management in University Campuses

5*S: Space, Surveyors & Students is a collaborative STEM outreach project lead by Maynooth University, in partnership with the Irish National Mapping Agency, Tailte Éireann, Technological University Dublin, Esri Ireland and the Society of Chartered Surveyors Ireland. Funded by Research Ireland and the European Space Education Research Office (Esero) Ireland, these groups have a shared interest to encourage student enrolment on 'geo' courses at university from under-represented groups and also to preempt a looming skills-gap. 5*S provides interactive and engaging educational content and training to teachers and students (12 to 18 years old) who are interested in learning more about satellites, spatial data and SDGs. Leveraging a combination of ArcGIS StoryMaps, a bespoke Augmented Reality app (SatelliteSkill5 - free to download on PlayStore and AppStore) and the National Geospatial Data platform, Geohive - students and teachers are provided with curriculum-focussed content that help teach how to harness the power of spatial data to solve a set of challenges. Framed around the United Nations Global Geospatial Information Management 14 Fundamental Geospatial Data Themes, each core piece of 5*S content topic is tailored to fit into a packed school curriculum and has been trialled in almost 20% of second level schools in Ireland. The learnings from this tailored content have been recorded and evaluated through a series of quantitative and qualitative respondent questionnaires and teacher focus groups/one-on-one interviews. The findings suggest cross curricular potential, value-add for schools and confirm the importance of this for encouraging data literacy and supporting teacher agency.

TorchGeo is a Python library bringing support for geospatial data to the PyTorch deep learning ecosystem. First released over four years ago, TorchGeo has always had strong support for 2D satellite image data. The upcoming TorchGeo 1.0 release will add complete time series support, including 1D through 4D data, requiring a complete rewrite of all GeoDatasets and GeoSamplers. This talk describes the 1.5 years of open source work required to enable full time series support and the backwards-incompatible changes coming to TorchGeo. It also demonstrates the power and simplicity of TorchGeo through a series of case studies: 1D) air pollution, 3D) change detection and land cover mapping, and 4D) weather forecasting and climate modeling. TorchGeo is open source and released under an MIT license, with over 140 built-in datasets, 130 foundation model weights, and 120 contributors from around the world.

The International Society for Photogrammetry and Remote Sensing Student Consortium (ISPRS SC) serves as the official representation of students and young professionals within ISPRS, connecting a global network of more than 900 active members from 64 countries as of November 2025. This paper presents a comprehensive overview of ISPRS SC activities during the 2022-2025 Board of Directors tenure, highlighting significant expansion in educational outreach and capacity building initiatives. Key achievements include facilitating 15 summer schools across seven countries, providing hands-on training in emerging geospatial technologies, and organizing more than 40 webinars through partnerships with 10 ISPRS Working Groups, demonstrating substantial growth from 2 webinars in 2022 to 24 in 2025. The consortium successfully launched 11 Student Chapters worldwide, establishing localized networks that promote inclusive access to geospatial education across diverse regions. Through quarterly publication of the SpeCtrum newsletter, maintenance of active social media presence across four platforms reaching over 10,000 followers, and organization of networking events at major ISPRS symposia, the consortium has strengthened its communication, networking and professional development opportunities. The paper also discusses operational challenges including funding constraints, geographic representation gaps, and Board capacity limitations, while outlining future initiatives including a mentorship program, virtual symposium, and comprehensive Congress 2026 activities. These efforts underscore ISPRS SC's evolving role in developing the next generation of geospatial professionals equipped to address global sustainability challenges.

The densification of GNSS Continuously Operating Reference Station (CORS) networks in mountainous regions is constrained by the high cost of geodetic-grade equipment. Low-cost (LC) multi-frequency GNSS receivers offer a viable alternative, yet their performance in challenging high-altitude Alpine environments remains largely unexplored. This study evaluates the rover-side positioning performance and tropospheric delay estimation capability of a newly installed LC permanent station at Prali (2200~m elevation), in the Alpine region of Piedmont, Italy. The station, based on a u-blox ZED-F9P receiver with a broadband LC antenna and a Raspberry Pi computer, was assessed using Virtual Reference Station (VRS) corrections from the SPIN3 professional CORS network. Six independent two-hour RTK sessions across a full diurnal cycle were processed using RTKLIB in forward-only kinematic mode to emulate real-time conditions. Results demonstrate that the LC station achieves centimetre-level horizontal precision (8--11~mm) with fix rates up to 97\% and time to first fix below 3~minutes under favourable conditions. A diurnal performance variability was observed and characterised across the six sessions. Zenith Tropospheric Delay estimation via CSRS-PPP with 92\% fixed ambiguities yielded physically consistent values (mean ZTD~=~1811~mm, ZWD~=~41~mm), consistent with dry winter conditions at altitude. These results confirm that LC GNSS stations can deliver reliable centimetre-level positioning and meaningful tropospheric products in demanding Alpine environments, supporting their deployment for CORS network densification in regions where geodetic-grade infrastructure is economically or logistically prohibitive.

Over the last few years immersive technologies have experienced rapid advancement providing several solutions in geospatial education such as improving student preparedness, enhancing student learning of theoretical concepts and practical procedures, and even supporting remote learning. However, several educators cannot utilize such immersive technologies because many of the

existing applications are not suitable for geospatial learning. Use of immersive technologies in education often necessitates specialized software and application development with the total investment (in terms of cost and time) becoming a barrier. This project is spearheaded by Working Group V/1 of ISPRS, and it is also supported by the Education and Capacity Building Initiative (ECBI) 2024 grant to provide sample experiences to educators. This project developed two immersive experiences relevant to geospatial education that can be used to enhance lab delivery and learning. The first experience uses a simplified GNSS receiver for

topographic mapping in virtual reality (VR). The second experience uses a tablet and an external GNSS receiver to visualize 3D objects in augmented reality (AR). To design these two applications the research team distributed a global questionnaire to professionals and educators. The questionnaire assisted in understanding the participant’s experience with immersive technologies, their attitude and beliefs towards these tools, and the potential benefits that immersive technologies can bring in education and industry. The results from the VR/AR implementation indicate that interactive environments can effectively support student preparation and reveal common misconceptions in topographic data collection, highlighting their value as both training and diagnostic tools in geospatial education.

The GeoRek project, funded by the DAAD within the German-Ukrainian University Network, aims to strengthen geospatial education in Ukraine through digitalization and international cooperation. Implemented by Jade University of Applied Sciences (Germany) together with Kyiv National University of Construction and Architecture (KNUCA), Dnipro University of Technology, and the University of Bamberg, the initiative develops innovative e-learning tools and micro-credential systems for geodetic reconstruction and high accuracy documentation.

A central element of the project is the VRscan3D - virtual laser scanner simulator — an educational platform that enables realistic training in terrestrial and mobile laser scanning without the expensive equipment. The system supports interactive learning, gamified exercises, and data export for advanced processing.

GeoRek further establishes micro-certificates in key subjects such as terrestrial laser scanning, photogrammetry, and 3D/BIM data processing, aligning with European standards (ECTS, EQF) to promote flexible and lifelong learning. The project’s applied component includes real-life case studies on the digital documentation for reconstruction of war-damaged buildings in Ukraine.

Overall, GeoRek exemplifies how modern digital education can strengthen academic resilience, support reconstruction, and deepen long-term German-Ukrainian cooperation in geospatial sciences.

Outbreaks of the European spruce bark beetle (Ips typographus) have intensified across Central and Northern Europe due to droughts, storms, and other extreme climatic events. Resulting Norway spruce mortality has reduced growing stock and weakened forest carbon uptake, creating an urgent need for rapid, operational tools for early detection. Pre-emergence detection, i.e. identifying infested trees before brood emergence, is particularly valuable, yet field surveys remain too slow and costly at large scales. UAV-based optical remote sensing offers high-resolution, flexible, and timely mapping at the single-tree level, allowing detailed observation of spectral changes soon after attack.

Despite many recent UAV studies, the reliability and transferability of pre-emergence detection remain unclear. Differences in sensor types (multispectral vs. hyperspectral), band configurations—especially in the red-edge and green-shoulder regions—and analytical approaches have produced inconsistent results. Many models are developed within single sites and often lack standardized accuracy metrics or cross-site validation, limiting insights into robustness under varying ecological and climatic conditions.

To address this, we compiled six UAV datasets from four major outbreak regions—southern Sweden, southern Finland, the southeast Alps in Italy, and Czechia—covering multispectral and hyperspectral campaigns at the single-tree level. Using these harmonized data, we compare machine-learning models for classifying tree health based on spectral features and vegetation indices. A central focus is transferability. We test models across regions using cross-regional, joint, and leave-one-region-out schemes to quantify generalization across contrasting climates, outbreak phases, and stand structures. The results reveal consistently informative spectral regions and modelling strategies, offering practical guidance for operational early-warning systems.

Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised differential vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model benefits most from the local context information during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.

The exponential spread of the bark beetle (Ips typographus L.) outbreaks across Europe in recent years has led to heightened interest in remote sensing-based detection. This increase is closely linked with ongoing climate change, which has led to rising temperatures, prolonged dry periods, and increasing frequency and intensity of both biotic and abiotic disturbances. These conditions created a favourable environment for bark beetle proliferation, resulting in larger and more widespread infestations. Effective detection and management of these outbreaks is crucial for forest officals, necessitating the implementation of monitoring systems that complement traditional ground-based efforts. At present, remote sensing approaches for bark beetle detection mainly rely on optical data to identify changes in spectral reflectance of vegetation. In this study, we utilised annual Sentinel-1 synthetic aperture radar (SAR) composites from 2021 to 2023 for infestation detection. A Random Forest classification model was applied to distinguish between healthy and infested forest areas. Additionally, vegetation indices were incorporated to evaluate and compare the results. A reference dataset was used to validate model performance. Our results show that the Sentinel-1 based approach achieved lower accuracies (max. overall accuracy: 0.78), compared to Sentinel-2 (max. overall accuracy: 0.87). Despite this, the Sentinel-1 data proved valuable as a tool for bark beetle infestations detection, especially in scenarios where optical data may be unavailable or limited. These results underscore the importance of integrating SAR data into remote sensing workflows to improve the detection of bark beetle outbreaks.

Forest ecosystems are increasingly threatened by biotic and abiotic disturbances that are intensifying under a changing climate. Accurate detection of tree stress is essential for effective forest management, as stress strongly increases vulnerability to damaging agents such as pests, pathogens, and fire. Tree water functioning is a key indicator of physiological status, yet traditional field-based methods for monitoring water transport – such as sap flow measurements – require costly instrumentation and can only be applied to a limited number of trees.

Hyperspectral remote sensing offers a powerful means to upscale forest health monitoring, but its effectiveness depends on robust spectral indicators that reliably reflect physiological change. Green-Shoulder Indices (GSI), which leverage reflectance features in the 490–560 nm region linked to carotenoid dynamics, have been previously used to monitor tree health. Because carotenoids are closely tied to photosynthetic regulation, stress responses, and canopy vitality, GSI have emerged as promising indicators of health decline. Notably, they have shown strong performance in detecting Norway spruce trees in the early stages of bark beetle infestation.

This study investigates how GSI can be further strengthened as indicators of forest hydraulic functioning by integrating hyperspectral drone imagery with continuous sap flow monitoring. By linking canopy spectral responses to internal water transport dynamics, we aim to advance GSI as operational tools for large-scale forest health surveillance and disturbance detection.

Carotenoids regulate photoprotection and respond early to stress, but their retrieval from canopy reflectance is often unstable because green-band signals are confounded by canopy structure, illumination/view geometry, and covariance with chlorophyll. This study proposes and evaluates the sensitivity of green-shoulder indices (derived from 490–550 nm bands) to carotenoid content in vegetation. We use FluSAIL simulations to generate canopy reflectance under wide-ranging biochemical and structural conditions and benchmark multiple green-region indices (490–560 nm, including PRI-type formulations) for their sensitivity and stability to carotenoids. We then transfer the best-performing index–carotenoid relationship to independent real-world datasets with pigment measurements at both leaf and canopy scales (ANGERS, LOTUS, CABO) to test generalization beyond the simulation domain. Results showed that a curvature-based green-shoulder index provided the most consistent carotenoid sensitivity, with the strongest and most stable VI–Car relationships across varying chlorophyll–carotenoid coupling, LAI, and sun–sensor conditions. Validation on measured spectra confirms that green-shoulder indices can predict carotenoid content with high accuracy and improved transferability compared with conventional green indices.

Non-stand replacing (NSR) disturbances—low- to moderate-severity events causing single-tree mortality or canopy thinning—are driven by agents such as drought, insects, pathogens, low-intensity fire, wind, and snow. Their variable duration, frequency, and extent challenge detection using medium-resolution optical imagery because changes are spectrally subtle and spatially complex. We developed a framework to detect NSR disturbances in boreal forests on a sub-annual basis using Sentinel-2 (S2) time series. Key methods include the spectral normalisation of monthly cloud-free composites via weighted multidimensional medians (medoid and geometric median), as well as improvements to the sensitivity and robustness of the HILANDYN algorithm. Observation weights are based on spectral distance measures (Euclidean distance and Spectral Angle Mapper), normalised using an adaptive sigmoid function. Normalisation reduced seasonality patterns by 41.4%, leaving only 13.7% of the tested time series with a significant seasonal pattern. Validated on more than 10,000 points, the best F1 and F2 scores were 0.75 and 0.72, respectively, when using seven S2 variables. These metrics increased to 0.80 and 0.81, respectively, when including detections in the subsequent vegetative season. The geometric median outperformed the medoid, and the optimal spectral indices varied by agent, e.g., NBR for canopy removal, red-edge indices for wind and snow damage. While the framework effectively maps natural and anthropogenic NSR events, reducing detection lag at high latitudes remains a priority.