Conference Agenda

Semantic 3D indoor mapping often depends on supervised learning and large annotated datasets, limiting scalability across diverse environments. This work introduces a category-specific prompt strategy for semantic 3D mapping using RGB-D cameras, integrating RGB-D SLAM with the Segment Anything Model 2 (SAM2) to enable annotation-efficient reconstruction. Keyframes and trajectories extracted from SLAM provide spatial references, while SAM2 performs zero-shot segmentation guided by a Category-Specific Prompt Strategy (CPSS), which segments structural and functional elements (e.g., floors, doors, staircases) by category to reduce prompt interference and manual effort. The segmented keyframes are then fused with depth and pose data to produce instance-level semantic point clouds. Experiments on custom RGB-D sequences and selected ScanNet scenes demonstrate centimeter-level geometric accuracy and strong semantic consistency, with mIoU values up to 0.89 on the custom dataset and 0.98 on ScanNet. The resulting semantic point clouds are clean, structured, and require minimal post-processing, showing that the proposed strategy provides an efficient and scalable solution for semantic 3D indoor mapping without retraining or environment-specific supervision.

Mobile robots are widely used in unmanned surveying, warehouse logistics, and emergency response. However, achieving safe, reliable, and efficient autonomous navigation in unknown environments remains challenging, where accurate environment representation and feasible trajectory planning are crucial. This paper presents an autonomous navigation method integrating lightweight LiDAR mapping with real-time local planning for ground robots. At the perception level, an incremental single-frame point cloud update is used to accumulate and project locally traversable space, producing a lightweight obstacle map that preserves geometric accuracy while reducing planning computation. At the planning level, A* is employed to generate reference control points, and uniform B-spline curves are used to optimize the trajectory while enforcing kinematic feasibility and smoothness. At the control level, nonlinear model predictive control (NMPC) ensures accurate trajectory tracking by producing control commands that satisfy velocity and acceleration constraints. The framework also supports low-cost evaluation in simulation. Experiments in simulated forests, simulated indoor corridors, and real-world gardens and hallways show average navigation speeds of 2.24 m/s, 0.76 m/s, 0.43 m/s, and 0.38 m/s, respectively. Results demonstrate that the proposed method generates smooth, feasible, and safe trajectories and completes autonomous navigation and mapping tasks across diverse environments.

Place recognition is a crucial task for both robots and autonomous vehicles, facilitating positioning and loop closure within pre-built global maps. Although single-modal sensor-based methods have shown satisfactory performance, cross-modal place recognition—retrieving low-cost camera images from global point cloud databases—remains a significant challenge. In this paper, we propose a contrastive learning-based lightweight cross-modal place recognition framework (CMCL-PR) to retrieve a single image from a global offline point cloud map. We introduce a perspective projection based field-of-view(FoV) transformation module that converts point clouds into a modality analogous to images; Then, we design a dual branch intra-modal encoder structure based on shared Transformer, which extracts and aligns image and point cloud features separately, effectively unifying the feature distribution between modals; Besides, a cross-attention mechanism module guided by inter-modal consistency was constructed, which utilizes the contribution of scene context information within different modalities to generate the discriminating cross-modal descriptors. Finally, during the contrastive learning, cross-modal feature was enhanced, and a multi loss function was constructed, including cross-modal contrastive learning loss, intra-modal consistency loss, and matching supervision loss. We assess the effectiveness and generalizability of our method using three publicly available datasets: KITTI, KITTI-360, and Oxford RobotCar. The project page and code will be released at https://github.com/qp-li/CMCL-PR.

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

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

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

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

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

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

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

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

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

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

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

We present the first quantitative evidence of systematic daily dead tree branch movement under field conditions, which has been previously only observed qualitatively. Using a boreal forest Permanent Laser Scanning (PLS) dataset, we investigated whether such movement can be detected and characterized in 3D laser scans. We link branch anatomy and geometry with environmental drivers, leading to the hypothesis that branch bending is proportional to wood moisture content. Hourly point clouds collected over 3.5 days from 17 dead branches (16 coniferous), attached mainly to living trees, were analyzed under calm weather conditions. We developed a novel workflow that tracks movement via non-rigid registration, calculates the angle between each branch and the vertical, and quantifies branch movement uncertainties across epochs. After accounting for time lags, these movements were related to the modeled wood moisture content using a linear mixed model. Clear and consistent daily movement was detected in all branches, with a mean amplitude of 21 cm and an average delay of 3.5 hours relative to moisture content changes. All branches moved downward during the day and upward at night, except one deciduous branch displaying the opposite pattern, consistent with our theoretical framework. The linear mixed model revealed a significant positive linear relationship between branch movement and wood moisture content. Our findings confirm that daily dead tree branch movement is primarily hygroscopic and demonstrate its effective detection using operational PLS. These insights open new possibilities for monitoring tree vitality using hypertemporal 3D sensing.

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3D-printed ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Urban trees provide critical cooling benefits and regulate microclimates in cities, yet their three-dimensional thermal behaviour remains difficult to visualize and communicate to stakeholders. Traditional approaches rely on 2D thermal imagery analyzed on desktop screens, lacking spatial context and the capability for temporal monitoring across phenological stages. This work presents a pipeline combining Gaussian Splatting with augmented reality to enable immersive, on-site visualization of urban tree thermal patterns.

We captured thermal infrared (TIR) images of silver linden trees (Tilia tomentosa) at the University of Strasbourg using a FLIR T560 thermal camera. The TIR images were preprocessed and then processed using MILo (Mesh-In-the-Loop Gaussian Splatting), generating 3D thermal meshes with approximately 3 million vertices. The reconstruction is validated against reference thermal point clouds acquired with a terrestrial laser scanner equipped with an integrated thermal camera, assessing both geometric completeness and thermal attribute fidelity.

The thermal meshes are deployed in a mobile augmented reality application, allowing users to visualize temperature distributions directly overlaid on physical trees in the field. This work demonstrates the first application of 3D Gaussian Splatting to thermal vegetation modelling, providing an engaging educational tool to communicate urban trees' cooling role while offering researchers a platform for detailed thermal analysis and forest health monitoring.

To robustly support glacier calving monitoring at high temporal resolution and enable future AI-based calving forecasts, this study presents an optimized Multi-Epoch Multi-Imagery (MEMI) strategy for automated 4D point cloud model generation. To date, the dataset comprises over 160,000 images acquired since December 2024 by an autonomous multi-camera system operating at 30 min intervals at Glacier Perito Moreno (GPM), Argentina. Despite high scene variability and harsh environmental conditions, the proposed MEMI workflow effectively addresses constraints imposed by continuous glacier motion and image degradation. The enhanced strategy aims to generate precise dense clouds with high alignment accuracy and computational efficiency, forming the basis for subsequent analysis of glacier front evolution. To achieve this, various parameter configurations are evaluated, including AI-based image masking and adaptive, optimized alignment-adjustment settings. Results from a representative eight-day subset show that variations in the tie point computation strategy lead to measurable differences in alignment-adjustment efficiency, with the best configuration being about 11 % faster than the least efficient one. By contrast, adaptive alignment-adjustment consistently improves alignment accuracy. Moreover, masking enhances both image quality checking and reconstruction quality, and, albeit modestly, improves pre-failure deformation analysis. Furthermore, daily seasonal responses to alignment are observed, as accuracy varies with solar illumination relative to the camera positions. Applying the optimal configuration to 260 MEMI projects in under 42 h produced 518 high-precision dense clouds and detected calving retreat magnitudes of up to 17.5 m, demonstrating the robustness and scalability of the proposed MEMI strategy for high-temporal-resolution 4D point cloud generation.

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

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

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

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

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

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

Urban digital twins are virtual city replicas that can greatly support urban planning by simulating infrastructure and mobility scenarios. However, keeping a digital twin up-to-date with fine-grained, real-world urban conditions is challenging. This paper proposes a novel system that leverages multi-modal AI models to bridge the gap between physical urban data collection and a 3D city digital twin. In our approach, ordinary smartphones carried in vehicles act as mobile sensors, continuously capturing multi-modal data (road images, GPS coordinates, and speed). Advanced vision-language models then analyze the data to automatically extract information from the traffic infrastructure and detect road anomalies. The extracted information such as the locations of traffic signs, traffic signals, road surface cracks, and potential blind spots at intersections is geo-tagged and streamed into language-vision models to interpret data and stream human readable insights into the digital twin model. The case study is the digital twin of the city of Toronto. By aggregating data from many drivers and analyzing it (in post-processing for high accuracy), the digital twin evolves into a living model of the urban environment. This enriched and dynamic twin provides urban planners with up-to-date insights on traffic signage, road conditions, and other relevant road infrastructure elements, enabling proactive maintenance and informed decision-making for city planning.

Methods that synthesize indoor 3D scenes from text prompts have wide-ranging applications in film production, interior design, video games, virtual reality, and synthetic data generation for training embodied agents. Existing approaches typically either train generative models from scratch or leverage vision-language models (VLMs). While VLMs achieve strong performance, particularly for complex or open-ended prompts, smaller task-specific models remain necessary for deployment on resource-constrained devices such as extended reality (XR) glasses or mobile phones. However, many generative approaches that train from scratch overlook the inherent graph structure of indoor scenes, which can limit scene coherence and realism. Conversely, methods that incorporate scene graphs either demand a user-provided semantic graph, which is generally inconvenient and restrictive, or rely on ground-truth relationship annotations, limiting their capacity to capture more varied object interactions. To address these challenges, we introduce GeoSceneGraph, a method that synthesizes 3D scenes from text prompts by leveraging the graph structure and geometric symmetries of 3D scenes, without relying on predefined relationship classes. Despite not using ground-truth relationships, GeoSceneGraph achieves performance comparable to methods that do. Our model is built on Equivariant Graph Neural Networks (EGNNs), but existing EGNN approaches are typically limited to low-dimensional conditioning and are not designed to handle complex modalities such as text. We propose a simple and effective strategy for conditioning EGNNs on text features, and we validate our design through ablation studies.

The Low-Altitude Economy (LAE), driven by the widespread deployment of UAVs and eVTOL aircraft, demands a high-fidelity Digital Twin that extends far beyond static geographic representation. This study presents a critical review of 39 peer-reviewed papers to propose a three-layer mapping framework — Geospatial Infrastructure Layer, Environmental Sensing Layer, and Interaction Layer — and evaluates the Technology Readiness Level (TRL) of each sub-domain. The Geospatial Infrastructure Layer encompasses terrain models, ground facilities, airspace structures, and semantic navigation landmarks. The Environmental Sensing Layer covers electromagnetic modeling, target sensing and countermeasure, and micro-meteorological mapping. The Interaction Layer addresses network trust, data security, swarm coordination, and platform reliability. Our TRL assessment reveals that Environmental Sensing is the most mature layer (mean TRL 4.4, 8 field-validated papers), while cross-layer integration remains the weakest link (mean TRL 3.5, zero field-validated demonstrations). We identify standardization of low-altitude spatial data products, AI-enabled predictive mapping, crowdsourced Digital Twin updating, and closed-loop cross-layer integration as the four priority research directions.

Indoor pedestrian localization on smartphones requires a lightweight yet robust fusion framework capable of handling unstable wireless signals and drift-prone inertial motion. In this work, we propose an efficient positioning system that integrates weighted Wi-Fi fingerprinting with Pedestrian Dead Reckoning (PDR) through a Multi-Step Extended Kalman Filter (EKF). Unlike conventional single-step filtering, the EKF employed here is formulated from the perspective of factor-graph–based probabilistic estimation, where Wi-Fi observations and PDR increments naturally act as complementary measurement and motion factors. Building on this unified view, the proposed Multi-Step EKF retains several consecutive states within its estimation window, effectively approximating short-horizon smoothing while maintaining the computational footprint required for real-time execution on consumer smartphones.

To enhance observation stability, an inverse-distance weighted fingerprinting module mitigates RSSI fluctuations and gracefully handles missing values. Meanwhile, PDR inputs are refined through diagnostic analysis and incorporated as motion constraints within the fusion process. Global optimization of noise parameters is performed via dual annealing, further improving the reliability of state updates.

Experiments conducted on an open indoor dataset demonstrate that the proposed method achieves a reduction of approximately 31% in mean localization error compared with a standard single-step EKF baseline. The results confirm that enforcing short-term temporal consistency through a multi-step state representation significantly suppresses drift accumulation and enhances robustness under dynamic indoor environments. Overall, the proposed framework offers a theoretically grounded, computationally efficient, and practically deployable solution for smartphone-based indoor positioning.

This project, a collaboration between the Landesamt für Geobasisinformation Sachsen (GeoSN) and M.O.S.S. Computer Grafik Systeme GmbH, pioneers the development of a Digital Twin Forest prototype for Saxony. The initiative leverages high-resolution aerial orthophotos (DOP) and advanced AI methods to generate detailed, current forest information. The core methodology centers on the “DeepTrees” workflow, a convolutional neural network (CNN)–based approach developed by the Helmholtz Center for Environmental Research (UFZ). This workflow processes DOP imagery at 10–20 cm resolution to segment individual tree crowns and extract key forest indicators, including crown area, crown radius, and tree density.

The process unfolds in three main stages: (1) preprocessing and model adaptation using transfer learning, (2) inference and postprocessing for accurate tree segmentation, and (3) integration into GeoSN’s data infrastructure. This integration utilizes OGC-compliant services and moGI-based data management, enabling automated processing, configuration, and visualization.

Results from the prototype confirm the feasibility of precise, large-scale tree crown segmentation from aerial imagery. The system also demonstrates the potential to derive temporal and structural forest information from recurring DOP datasets. These outputs can be directly incorporated into operational geospatial systems, supporting climate adaptation, forest management, and policy-making.

In conclusion, the Project showcases how explainable, interoperable AI workflows can strengthen national geodata infrastructures and serve as a model for future federated, AI-driven digital forest twins across Germany.

This study presents a scalable framework for mapping aboveground biomass and moisture content in peatlands using intensive field sampling, multi-sensor satellite imagery, and advanced machine learning. Field data collected from diverse bog and fen sites in Western Newfoundland are integrated with Sentinel-1/-2 synthetic aperture radar and optical data, complemented by 3 m PlanetScope imagery for site-level detail. Ensemble learning models, particularly XGBoost, yield high biomass mapping accuracy, with regional maps revealing major biogeographical gradients and fine-scale site mosaics. Feature importance analysis highlights the role of red-edge and SAR bands in prediction. The results demonstrate that free satellite archives and machine learning can overcome limitations of costly airborne campaigns, supporting operational carbon monitoring and ecological management in northern peatlands. This approach establishes a foundation for wide-area wetland monitoring and future expansion using emerging remote sensing technologies.

Surface soil moisture (SM) plays a significant role in environmental and hydrological processes, particularly runoff and evapotranspiration. Within forest ecosystems, changes in SM can lead to significant ecological impacts, including paludification and greater susceptibility to forest fires. Microwave remote sensing facilitates large-scale monitoring of SM. Moreover, machine learning (ML) have demonstrated strong potential for capturing the nonlinear relationships between SM and satellite data. In general, supervised ML techniques achieve higher success rates when trained on larger ground measurements. However, obtaining extensive ground measurements of SM over vast areas such as forests is challenging, expensive, and time-consuming. To address this limitation, this study proposes a self-supervised method based on pre-task learning to estimate SM over forested areas using multisensor data. The core idea of the self-supervised approach is to leverage the knowledge gained during pre-task learning from multisensor data and transfer it to the SM estimation task, thereby improving the model’s generalization ability to SM estimation. The self-supervised learning method achieved an overall coefficient of determination (r²) of 0.74 and an RMSE of 0.04 m³/m³ on the testing dataset By focusing on each forest site, the model obtained r² = 0.75 with RMSE = 0.04 m³/m³ at Millbrook, r² = 0.63 with RMSE = 0.04 m³/m³ at Massachusetts, and r² = 0.74 with RMSE = 0.03 m³/m³ at Saskatchewan. The results highlight the potential of multisensor data for SM estimation in forested areas. Our method, which utilizes self-training on the input data, reduces dependence on ground SM measurements and enhances generalization capability.

Land cover data need to be updated regularly. Typically, remote sensing images (RSI) play a central role in this process. A first step is RSI semantic segmentation. Today, this task is mainly solved by deep learning. Especially vision foundation models (VFM) have gained increasing importance in this context. Having been trained on large datasets, VFM for segmentation can yield good results on data from various domains without further training. We present a new method for using the VFM Segment Anything Model 2 (SAM 2) for multi-class semantic segmentation of Sentinel-2 images that does not require training data. Our method is based on a prompt engineering approach, using SAM 2 in its pre-trained form. The different prompt types are generated on the basis of existing topographic data. We also propose a post-processing step for merging the output of SAM 2 to obtain a multi-class label image. The results of our experiments show that our method achieves an overall accuracy (OA) of up to 93% at pixel-level using mask prompts. Experiments with other Sentinel-2 3-channel composite images do not show significantly different results compared to R-G-B images. Incorporating data from different time steps, intended to be used for map updating, shows good results. But the small amount of changed areas indicate limitations. In general, the proposed method is suitable for further

research into semantic segmentation tasks with little or no training data, as well as for the process of updating databases.

This paper presents a radar-centric multi-sensor fusion framework, RLIO, designed for robust indoor SLAM in perceptually challenging environments such as underground garages and smoke-filled areas. Unlike conventional LiDAR-based methods that degrade under poor visibility, RLIO tightly integrates 4D imaging radar, 3D LiDAR, and an IMU within an iterated extended Kalman filter. The system introduces three key modules: a motion-prior-driven radar velocimetry algorithm for stable velocity estimation, a velocity-prior-enhanced scan-to-map registration for drift reduction in degenerate geometries, and an adaptive fusion strategy that dynamically adjusts sensor weights based on real-time degradation detection. Experimental results from both handheld and UGV platforms demonstrate that RLIO achieves accurate localization and high-quality mapping even when LiDAR performance deteriorates due to smoke or repetitive structures, highlighting its potential for reliable all-weather autonomous navigation and mapping in complex indoor and outdoor environments.

4D radars have attracted increasing interest for robotic perception because they remain effective in adverse conditions such as darkness, dust, smoke, rain, and fog. Compared with conventional automotive radars that mainly provide planar coordinates and relative Doppler velocity, modern 4D radars also sense elevation, which makes them more suitable for geometric odometry and mapping.

In this paper, we propose RAMBA, an offline 4D radar mapping framework based on bundle adjustment. Starting from initial poses and radar frames produced by a radar--inertial odometry front-end, we refine the radar frame states to improve global mapping consistency, measured by covariance-weighted point-to-point distances. In essence, our method extends pairwise generalized iterative closest point (GICP) to the multi-frame setting. Candidate correspondences are formed within voxels of a voxel grid built from all selected frames, and each residual is weighted by the sum of the two point covariances. The geometric constraints are jointly optimized with IMU preintegration and radar ego-velocity constraints.

To reduce false associations caused by drift and revisits, RAMBA enforces temporal consistency when forming correspondences and explicitly allows constraints around loop closures. We evaluate the method on the ColoRadar and SNAIL Radar datasets. The proposed refinement consistently improves map quality and usually improves trajectory accuracy over the initial radar--inertial odometry and pose graph optimization. To the best of our knowledge, this is the first geometric offline bundle-adjustment framework for consistent 4D radar mapping.

Four-dimensional (4D) imaging radar offers robustness in adverse weather and lighting, but its point clouds remain sparse, noisy, and affected by ghost reflections, making geometric scan matching unstable. This work integrates two existing deep correspondence models—Radar Transformer and RPM-Net—into a radar–inertial odometry pipeline without retraining. Both networks run asynchronously in dedicated ROS nodes: radar and submap point clouds are cropped, transformed, and sent to the matchers, which return either hard or soft correspondences for the first IEKF iteration of each frame. When neural outputs are delayed, the system automatically falls back to geometric matching.

Returned matches are fused with a voxelized IEKF backend that computes Mahalanobis-weighted residuals. RPM-Net further supplies soft targets and confidence weights, enhancing point-to-point constraints. Experiments on ColoRadar indoor and outdoor sequences show that learning-based correspondences can reduce drift in weakly structured scenes while maintaining robustness when geometry is reliable.

This paper presents MSMR SLAM, a multi sensor and multi robot SLAM framework that integrates LiDAR, 4D radar, and IMU to achieve robust localization and consistent mapping in large scale and degraded environments. The front end includes radar inertial odometry and LiDAR inertial odometry, and evaluates the reliability of LiDAR observations using point cloud sparsity and effective range. An adaptive fusion module combines the two odometry estimates to maintain stable state estimation, while radar assisted dynamic point removal improves the reliability of geometric constraints. The back end constructs a unified factor graph that incorporates multi sensor odometry constraints, loop closure factors, and inter robot association factors. A LiDAR centered and radar assisted matching strategy enhances cross robot data association, and radar based loop closures improve global consistency when LiDAR measurements degrade. The system maintains a dense LiDAR map together with a complementary radar map, enabling hybrid mapping that remains reliable in perceptually challenging regions. Experiments on campus datasets, including smoke filled scenarios, demonstrate that MSMR SLAM achieves high precision multi robot localization and globally consistent mapping. Compared with single robot baselines, the proposed framework provides improved accuracy and robustness, and the integration of LiDAR and radar yields more complete and stable map reconstruction in complex environments.

1. We introduce a novel framework for 4D radar-camera extrinsic calibration that utilizes commonly available dynamic objects as natural correspondences.

2. We develop a heuristic-guided strategy to reliably associate radar points with image detections and estimate the extrinsic parameters without deep learning.

General-purpose spatial representations are essential for building transferable geospatial foundation models (GFMs). Among them, the AlphaEarth Foundation (AE) represents a major step toward a global, unified representation of the Earth's surface, learning 10-meter embeddings from multi-source Earth Observation (EO) data that capture rich physical and environmental patterns across diverse landscapes. However, such EO-driven representations remain limited in capturing the functional and socioeconomic dimensions of cities, as they primarily encode physical and spectral patterns rather than human activities or spatial functions. We propose AETHER(AlphaEarth–POI Enriched Representation Learning), a lightweight framework that adapts AlphaEarth to human-centered urban analysis through multimodal alignment guided by Points of Interest (POIs). AETHER aligns AE embeddings with textual representations of POIs, enriching physically grounded EO features with semantic cues about urban functions and socioeconomic contexts. In Greater London, AETHER achieves consistent gains over the AE baseline, with a 7.2% relative improvement in land-use classification F1 and a 23.6% relative reduction in Kullback–Leibler divergence for socioeconomic mapping. Built upon pretrained AE, AETHER leverages a lightweight multimodal alignment to enrich it with human-centered semantics while remaining computationally efficient and scalable for urban applications. By coupling EO with human-centered semantics, it advances geospatial foundation models toward general-purpose urban representations that integrate both physical form and functional meaning.

This work demonstrates the potential of foundation-model-encoded satellite embeddings to bridge Earth's surface and atmosphere. Based on our multimodal foundation model Copernicus-FM, we curate a global embedding dataset at 0.25°x0.25° resolutions (in consistency with ERA5). For each grid, multi-sensor images from Sentinel-1, 2, 3, 5P, and DEM are encoded into an embedding vector. These grid embeddings serve as condensed surface representations for downstream users. We verify their benefits as input for a climate task that predicts the 10-year mean and standard deviation of several climate parameters (e.g., precipitation) from ERA5. Compared to raw coordinates or location encodings, our results suggest that introducing surface embeddings helps produce more accurate prediction maps, reducing RMSEs by an average of up to 45%.

Ground-level horizon imagery is scarce across planetary bodies, making representation-centred approaches attractive for downstream geospatial tasks. We present DESPINA, a geospatial representation system that converts digital elevation models (DEMs) into structured neural embeddings of terrain geometry that condition a diffusion model to produce geometry-preserving, terrain-consistent visual reconstructions for a specified location and view direction.

Our pipeline integrates numeric elevation data (DEMs), structural embeddings (inverse-depth and soft edges), and textual priors, unifying heterogeneous geospatial signals into a shared, metric conditioning space. Using a Stable Diffusion model constrained with ControlNet, we can generate geologically consistent yet texturally diverse horizon datasets. Appearance priors are learned from historical surface photography to capture realistic textures and lighting cues, and geometric validation is performed against DEM-derived skylines and depth structure, independent of photographic training data. Through quantitative evaluation and a pilot qualitative study, DESPINA maintains skyline fidelity and geological boundaries while improving structural similarity relative to an image-conditioned baseline. Although our experiments use lunar DEMs and historical surface photography, the method is domain-agnostic and applicable to Earth, Mars, and other planetary DEMs.

Satellite -based crop classification and maps are important tools for food security and climate change mitigation, but existing approaches are not effective for small field systems. To address this, crop type classification using embeddings generated by a global foundation model, TESSERA, are compared to standard classification approaches in the literature. We find that our embedding -based approach offers a triple win: 1) consistent and statistically significant performance improvement over current methods, 2) greater simplicity due to the elimination of feature engineering, and 3) the reduction of computational cost. Our embedding -based approach achieves significantly higher F1 scores in the classification of 5 of 7 crop types for small fields in Austria (over 10% improvement in one case). Additionally, the TESSERA embedding -based method uses 8% of compute compared to the raw data method. These results indicate that embeddings are an effective approach for crop type classification tasks in small field systems.

This study explores the potential of the AlphaEarth Foundation (AEF) embeddings, a global, annual, analysis-ready satellite embedding dataset, for winter wheat and crop rotation mapping. Firstly, we analyze AEF embeddings for intra-class consistency and inter-class separability, assessing their effectiveness in representing wheat within the semantic embedding space. Subsequently, we compare multiple lightweight classifiers to identify an optimal model and conduct spatiotemporal generalization experiments across Henan Province from 2018 to 2024 using only a limited set of labelled samples from 2020. Based on the resulting wheat distribution maps, crop rotation patterns are further identified.Experimental results demonstrate that AEF embeddings exhibit strong semantic coherence and discriminative capability. Acceptable classification accuracy (OA = 0.85) can already be achieved using simple models such as cosine similarity and linear regression. More advanced lightweight classifiers further improve the performance (OA = 0.86–0.93) while maintaining stable results across different years and regions (spatial consistency = 0.82). In addition, the crop rotation maps show high spatial agreement with existing products, while producing more spatially contiguous field patterns.Overall, this study confirms that AEF embeddings can serve as effective, ready-to-use features for large-scale agricultural remote sensing applications. By substantially reducing the reliance on complex feature engineering and extensive training samples, they provide a practical and scalable solution for mapping winter wheat and its crop rotation patterns.

Identifying a distressed vessel among many ships detected in wide-area aerial imagery is a critical challenge in maritime Search and Rescue (SAR) operations. Conventional methods cannot determine which vessel matches the incident description, especially when Automatic Identification System (AIS) reports are uncertain. This study proposes an integrated framework that combines MLLM-based semantic attribute extraction with geolocation fusion to prioritize candidate vessels according to their consistency with Situation Report (SITREP) based scenarios. The method detects vessels using YOLOv8, tracks them with Deep Simple Online and Real-time Tracking (DeepSORT), and performs image-based georeferencing using onboard metadata. A Multi-modal Large Language Model (MLLM) extracts appearance/status attributes from representative vessel images, while scenario descriptions are also converted to attributes. Both sets are encoded using MiniLM embeddings. Finally, semantic similarity is fused with geolocation proximity within an Support Vector Machine (SVM) classifier to produce a probability-ranked list of candidates. Experiments using real aerial search footage demonstrate robust identification performance across a range of scenario quality levels. The correct vessel appears within the top three candidates in more than 73\% of cases and within the top five in more than 91\%, even when attribute extraction is affected by low resolution, illumination effects, or missing scenario information. These results show that coarse semantic cues, when combined with approximate geolocation, provide a resilient basis for identifying target vessels under high uncertainty. The proposed framework offers a practical foundation for automated SAR decision support, enabling faster and more reliable prioritization during wide-area maritime search operations.

Cross-view geolocalization (CVGL) systems, while effective at retrieving a list of relevant candidates (high Recall@k), often fail to identify the single best match (low Top-1 accuracy). This work investigates the use of zero-shot Vision-Language Models (VLMs) as rerankers to address this gap. We propose a two-stage framework: state-of-the-art (SOTA) retrieval followed by VLM reranking. We systematically compare two strategies: (1) Pointwise (scoring candidates individually) and (2) Pairwise (comparing candidates relatively). Experiments on the VIGOR dataset show a clear divergence: all pointwise methods cause a catastrophic drop in performance. In contrast, a pairwise comparison strategy using LLaVA improves Top-1 accuracy over the strong retrieval baseline. Our analysis concludes that VLMs are poorly calibrated for absolute relevance scoring but are effective at fine-grained relative visual judgment, making pairwise reranking a promising direction for enhancing CVGL precision.

Accurate and topologically consistent vector data extraction from aerial imagery is essential for geospatial applications such as urban mapping and the enrichment of volunteered geographic information (VGI) platforms like OpenStreetMap (OSM). While deep learning has advanced automated feature extraction, traditional supervised networks remain constrained by large annotation requirements and limited generalization. Recent vision foundation models offer a promising alternative through promptable, zero-shot segmentation capabilities.

This study presents a modular “pixels-to-polygons” workflow for transforming high-resolution orthophotos (20 cm DOP20) into GIS-ready vector data, comprising four stages: data preparation, segmentation, vectorization, and validation. The framework is model-agnostic and designed to integrate different vision foundation models without modification to downstream processing. As a representative instantiation, we evaluate the Segment Anything Model 2 (SAM2) in automatic mask generation mode over the University of the Bundeswehr Munich campus.

The resulting segmentation masks are polygonized and compared against OSM reference layers. Preliminary results indicate that vision foundation models can effectively delineate major built-up areas, vegetation, and transport infrastructure without task-specific training, producing geometrically smooth and topologically valid polygons. However, smaller or shadowed objects remain challenging, often leading to partial merging or fragmentation.

Future work will include quantitative evaluation using raster-based Intersection-over-Union and vector-domain metrics such as completeness, correctness, and geometric fidelity. The study provides an initial assessment of vision foundation models for high-resolution remote sensing and highlights their potential to bridge the gap between general-purpose segmentation models and GIS-compatible vector data generation.

Surface-laid unexploded ordnance (UXO) and landmines constitute a critical humanitarian crisis. While unmanned aerial vehicles (UAVs) provide a scalable remote sensing solution, detecting modern, non-metallic explosive devices in cluttered environments remains a profound Camouflaged Object Detection (COD) challenge. Traditional optical sensors frequently suffer from foreground-background confusion when a target's texture mimics its surroundings. To overcome these physical bottlenecks, we introduce XPol-Net, a novel multimodal architecture synergizing the semantic reasoning of Vision Transformers with the deterministic physics of polarimetric imaging. Built on a hierarchical PVTv2 backbone, XPol-Net utilizes a progressive Dual Cross-Attention Strategy for effective modality fusion. In early stages, Channel Cross-Attention (CCA) filters material-specific Degree of Linear Polarization (DoLP) cues to suppress background clutter. In deeper stages, Spatial Cross-Attention (SCA) dynamically aligns high-level RGB semantics with strict structural boundaries. To enhance robustness and prevent modality collapse, we deploy a multi-task auxiliary learning framework that reconstructs the continuous Angle of Linear Polarization (AoLP) map. On the PCOD benchmark, XPol-Net achieves state-of-the-art results in global structural alignment (E_phi of 0.980 and 0.984 at 352 x 352 and 704 x 704, respectively). While minor trade-offs are observed in localized metrics such as S_alpha or F_beta, XPol-Net remains highly competitive, consistently delivering superior results in E_phi and MAE. By prioritizing structural recall over localized strictness, XPol-Net ensures the complete discovery of concealed targets, establishing a reliable, physics-aware foundation for humanitarian demining operations.

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

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

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

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

Daily satellite-derived nighttime lights (NTL) are increasingly used to monitor electricity disruption and recovery, but their reliability in tropical regions is constrained by persistent cloud cover and intermittent observation. This study adopts a diagnostic-first framework that treats observability as a prerequisite for interpretation, determining when daily NTL signals are sufficiently supported to reflect underlying grid dynamics. Using NASA’s VIIRS Black Marble product, we quantify spatial and temporal completeness and radiance stability across the Samar–Leyte sub-grid in the Philippines. Results show that observations are highly intermittent and spatially heterogeneous, with urban areas providing more stable and interpretable signals, while rural regions remain noise-dominated.

To assess whether reliability-qualified NTL reflects electricity demand, DNB-BRDF radiance is aligned with hourly load data from the National Grid Corporation of the Philippines (NGCP) using settlement-based masks derived from GHSL SMOD. Alignment is evaluated using correlation, error, and retained coverage, combined into a composite score. Strongest agreement occurs under low to mid-range valid-pixel thresholds and within urban-focused masks, which balance signal fidelity and temporal continuity at the cost of reduced coverage. Replication across four additional Visayas sub-grids shows that optimal threshold–mask configurations vary by region, reflecting differences in cloud regime and settlement structure.

These results establish explicit conditions under which daily NTL can be interpreted as a proxy for grid dynamics. The framework provides a reproducible basis for reliability-qualified analysis using globally available datasets and can be tested in other cloud-prone regions where ground-based data are limited.

The maintenance of airport pavements is critical to ensuring the safety and efficiency of air operations. Conventional inspection methods are often time-consuming, subjective, and prone to inconsistencies in data collection. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a potential alternative for improving inspection efficiency and measurement accuracy.

This study evaluates the applicability of UAV-based photogrammetry for the detection and quantification of pavement distresses under conditions representative of airport infrastructure. Image data were acquired at different flight altitudes and overlap configurations and processed using Structure-from-Motion techniques to generate high-resolution orthomosaics and Digital Elevation Models (DEMs). The resulting datasets were analyzed to identify, delineate, and classify deterioration types and severity levels.

The results indicate that a flight altitude of 10 m combined with 80% longitudinal and 70% transversal overlap provides an optimal balance between spatial resolution and operational efficiency. Under unobstructed conditions, photogrammetric analysis detected more than 98% of existing distresses and enabled more precise geometric delineation compared to traditional field-based methods. Undetected distresses were primarily associated with shadowed or obstructed areas, highlighting the influence of environmental conditions on detection performance.

Overall, the findings demonstrate that UAV-based photogrammetry is a reliable and efficient approach for pavement condition assessment, with significant potential to enhance data quality and reduce inspection time in airport infrastructure management.

3D change detection is essential for monitoring infrastructure, environmental dynamics, and natural hazards. However, existing algorithms are often evaluated on single-scene datasets, and their generalization across varied real-world scenes remains largely unexplored due to the absence of a universal benchmark. To address this issue, we propose MultiChange3D, a multi-scene, multi-sensor 3D change detection dataset for identifying geometric changes in 3D space. The dataset provides registered pairs of point clouds with ground-truth geometric change labels, enabling standardized evaluation across different methods. To demonstrate the use of the MultiChange3D dataset, we benchmark an initial set of approaches on a subset of the dataset. The evaluated methods include classical Euclidean distance-based methods (C2C, M3C2), 3D displacement estimation-based approaches (F2S3, Landslide-3D), and deep learning-based classification methods (KPConv, EF-KPConv, PGN3DCD). Quantitative and qualitative analyses indicate the strengths and limitations of the evaluated methods, highlighting the challenges in cross-scene generalization under variations in point density, scale, and types of changes. The full dataset and evaluation code is openly available at: https://github.com/3DOM-FBK/multichange3d.

Quantifying topographic dynamics from 3D point cloud time series is essential for geoscientific applications. However, laser scanning data typically varies between epochs in point density due to differing survey properties. These irregularities present a challenge for change detection, particularly across multi-temporal and multi-modal data. We propose a new approach, adaptive temporal aggregation, as an extension of the Multiscale Model to Model Cloud Comparison (M3C2) algorithm. Driven by a local point density requirement, our method employs both a spatial and a temporal neighborhood. If a core point's neighborhood is too sparse for M3C2 estimation, an iterative temporal search progressively incorporates data from temporally adjacent epochs until the density requirement is met or a maximum temporal window is reached. This adaptive process ensures sufficient local density while preventing unnecessary temporal aggregation, a key advantage over global aggregation. We evaluated our method on a multi-modal dataset from the Mont-Saint-Michel Bay, France (38 irregular epochs, ~1 decade). Results demonstrate significantly improved change detection, increasing completeness by >13% (vs. standard M3C2) and accuracy by 31% (vs. fixed-window averaging). Our work provides a robust approach for enhancing 3D change detection algorithms for complex, real-world 4D datasets, enabling higher accuracy and completeness in analysing surface dynamics.

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.

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

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

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

The increasing demand for safe and efficient autonomous vehicle (AV) operations has intensified the need for realistic, high-fidelity digital road representations that enable robust virtual testing environments. Simulation-based validation has become a cornerstone of the AV development process, allowing for the reproducible assessment of perception, localization, and decision-making modules under controlled conditions. Within this context, the ASAM OpenDRIVE specification provides a standardized, XML-based description of static road networks, encapsulating geometric, semantic, and structural elements such as roads, lanes, junctions, and roadside objects. While previous research has primarily focused on the geometric accuracy and semantic richness of High Definition (HD) maps, comprehensive topological analyses—especially those addressing consistency, connectivity, and completeness of OpenDRIVE models—remain largely unexplored. This study aims to fill that gap by introducing a formal topological framework for evaluating OpenDRIVE-based road models through both synthetic and real-world test cases.

This paper presents a comprehensive toolkit for semi-automated High-Definition Maps (HD Maps) production that integrates Artificial Intelligence (AI)-driven feature extraction with 3D human-in-the-loop validation. High-definition maps provide centimeter-level road geometry and traffic asset information, but large-scale production remains costly due to dense mobile mapping data and manual digitization. The proposed workflow consists of two self-developed components: a Semi-automated HD Maps Production Tool for batch extraction and a 3D HD Maps Validation and Editing Tool for structured review. The project-based pipeline ingests georeferenced mobile laser scanning point clouds, Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) trajectories, and camera imagery, and applies configurable chains of ground filtering, road-marking extraction, voxel down-sampling, clustering, oriented bounding box analysis, and AI-based traffic asset detection. Candidate features with confidence indicators and basic attributes are stored in a project database and edited in a tightly coupled 3D environment that supports snapping, constrained adjustments, and semantic reclassification while logging all user edits. The toolkit is evaluated on a closed proving ground (CARLab, Shalun) and a freeway section of Taiwan National Highway No. 3. At CARLab, semi-automated extraction achieves F1-scores of 0.85–0.95 for key layers. For a one-kilometer highway section, operator time is reduced from 90–120 minutes in a purely manual Geographic Information System (GIS) workflow to about 45 minutes with the proposed approach, while maintaining comparable geometric accuracy. These results demonstrate a practical path towards scalable, traceable HD Maps production for autonomous driving applications.

National Key Research and Development Program of China (2025YFB3910300);

3D scene reconstruction lies at the core of computer vision, photogrammetry, and geospatial science, spatial intelligence, aiming for accurate, photorealistic, and efficient digital twin representations of the real world. The emergence of revolutionary 3D Gaussian Splatting (3DGS) enables real-time rendering and geometrically precise reconstruction, yet existing methods struggle in large-scale outdoor scenes with weak textures, low geometric accuracy, dynamic objects, and lack of semantic information. Therefore, geometrically accurate 3D GS with enhanced semantic understanding greatly facilities the realization of digital twins for mobility and navigation. This work proposes a novel 3DGS framework which seamlessly incorporates dense UAV LiDAR point clouds, multi-view images and open-set semantics in an all-in-one optimization process. The key objective here is to investigate how geometric constraints derived from dense UAV LiDAR point clouds and cognitive supervision from SAM (Segment Anything Model) semantics can jointly participate in the optimization of Gaussian primitives, thereby improving geometry accuracy, visual realism, and semantic consistency in large-scale UAV 3D reconstructions for creating digital twins of the environments.

The restoration of historic buildings is an important topic in today's society and constitutes the primary subject of this study. The Diji Altar, located along the central axis of Beijing, is not only a significant historical landmark but also an important remnant of China's ancient imperial sacrificial architecture. Although some studies have focused on the Diqi Altar, such as its ritual hierarchy and craftsmanship as recorded in historical texts, certain research gaps remain. Due to the damage to the altar structure and insufficient documentation in relevant literature regarding its structural form, platform base specifications, and stylistic evidence, systematic research on restoration techniques remains relatively scarce. There is a need to reconstruct evidence based on architectural principles. Addressing this critical gap is of great significance for understanding the technical achievements and ceremonial principles of official architecture during the Ming and Qing dynasties, and for guiding the restoration and preservation of ancient buildings.

The digital preservation of open-air sites often faces multiple challenges, such as dispersed components, varied forms, and missing historical records. In response, this study focuses on the Beijing Dizhitan and proposes and implements an innovative workflow that deeply integrates architectural morphology theories, archaeological typological methods, and modern digital technologies. This workflow systematically constructs a complete methodological chain, from the semantic annotation, classification, and virtual assembly of stone components, to the virtual restoration and model reconstruction of the site, ultimately achieving scenario-level restoration and display evaluation. The successful restoration of the Dizhitan demonstrates that this approach not only effectively "revives" dispersed components, placing them in their proper positions in a virtual space, but also pioneers a replicable new paradigm that embeds rigorous academic research throughout the digital process. This provides an entirely new technical approach and perspective for the preservation, study, and interpretation of immovable open-air cultural relics.

This paper addresses the limitations of single-modal data in rock art cultural heritage preservation, such as incomplete information and fragmented semantics. It proposes a method for constructing a multimodal dataset that integrates text, images, and 3D point clouds. Text data is structured and semantically annotated using the ArchaeoBERT model; image data is obtained through web scraping, annotation, and augmentation; and point cloud data is captured using laser scanning, noise reduction, and registration techniques. Feature mapping alignment is employed, combining CNN, BERT, and PointNet++ to extract features and generate unified vector representations. Through a three-level quality control process, the data is accurate and reliable, with information coverage increased by 47.3%. This dataset achieves comprehensive integration of semantic, visual, and spatial information, providing a multidimensional data foundation and practical reference for the digital preservation, 3D reconstruction, and cross-modal retrieval of rock art.

The rapid evolution of Monocular Depth Estimation (MDE) models — and in particular the emergence of recent foundation models such as Depth Anything v2 (Yang et al., 2024; Ranftl et al., 2022) — is opening concrete perspectives for the application of artificial intelligence in architectural and cultural heritage surveying.

This research aims to assess the feasibility of employing such models to obtain metric depth estimations from UAV imagery, acquired in both oblique and nadir views, with the broader goal of integrating neural networks into 3D documentation, HBIM, and GIS workflows for built heritage.

The Depth Anything v2 models were trained initially for ground-level scenarios, where the camera typically operates 1–2 m above the ground, with horizon distances extending up to 60–80 m. When applied to aerial imagery, particularly drone-based acquisitions, this results in a substantial domain gap: the network tends to interpret top-down landscapes as distant horizons, thereby compressing the depth scale.

To address this issue, this study develops an experimental calibration and adaptation procedure aimed at transforming the depth maps produced by the model into metrically consistent estimates that are coherent with architectural reality.

We present an approach to ensure trustworthy, high-quality GeoAI which requires a coordinated effort across academia, industry, government, and standards bodies. The ISPRS community, in partnership with organizations such as ISO, OGC, the World Geospatial Industry Council (WGIC), the International Society for Digital Earth (ISDE), and other international initiatives is uniquely positioned to host this dialog. By aligning emerging GeoAI practices with established data-quality standards and ethical-AI frameworks, the community can help shape a future-proof foundation for responsible innovation in geospatial intelligence.

This paper presents research on integrating Data Quality and Licensing metadata into Open Science workflows using ISO and OGC standards and machine-readable profiles to enhance interoperability, transparency, and reusability of scientific data. All if this is possible via ongoing development of modular building blocks, validation frameworks, and engagement with standards bodies to support FAIR principles and scalable data reuse across domains. Our approach demonstrates the possible integration of concept schemes and measures defined in the ISO 19157 multipart standard.

This paper presents the ISO 19157-3 Data quality measures register, discusses its design and implementation and illustrates its the utility to the ISPRS and wider geocommunity. In this paper we highlight the importance of providing geographic metadata about quality, the evolution of international standards to support this, and a novel implementation of a human readable and machine-actionable web register for geographic data quality measures.

The rapid growth of geospatial data, fueled by advancements in satellite imagery, IoT sensors, and mobile services, presents significant opportunities in sectors like urban planning and environmental monitoring. However, these data are also vulnerable to cyber threats, emphasizing the need for strong protection mechanisms. This paper introduces a modular, hybrid architecture that addresses security challenges by integrating post-quantum cryptography, decentralized storage, and access control via Blockchain. It employs AES-GCM for the secure encryption of large datasets and Kyber for enhanced key protection against quantum threats. Encrypted data is stored securely in the Interplanetary File System (IPFS), with access managed by smart contracts on a private Ethereum blockchain. The architecture utilizes FastAPI for back-end processes, microservices for cryptographic services, and React for the user interface. Performance assessments show good scalability and resilience, paving the way for secure geospatial data sharing while harmonizing data sovereignty, quantum security, and decentralized management.

High-Resolution Global Land Cover (HRLC) products are essential for monitoring Earth’s surface dynamics and supporting policy frameworks like the Sustainable Development Goals. Recent global products such as ESA WorldCover, ESRI LULC, FROM-GLC, and Dynamic World offer 10–30 m resolution maps, but their interoperability remains limited due to differences in input data, class legends, and validation protocols. This lack of harmonization hampers cross-comparison and integrated use for environmental monitoring. Although advances in remote sensing, AI, and cloud computing have enabled more frequent and detailed mapping, they have also introduced new challenges for ensuring data consistency and comparability.

Validation of HRLC products is hindered by the absence of a common benchmark dataset, as current accuracy metrics are derived from heterogeneous reference samples and class definitions. Traditional validation methods are costly and time consuming, while temporal inconsistencies and cloud contamination further increase uncertainty. ISO 19157-3 offers a standardized framework to describe and automate quality measures such as positional accuracy and thematic correctness, supporting transparent and reproducible evaluation across datasets.

A sustainable solution involves establishing an international benchmarking framework with standardized reference data, legends, and sampling strategies. As a practical interim approach, the Map of Land Cover Agreement (MOLCA) combines multiple HRLC products to identify spatial consensus and disagreement, offering a proxy for thematic reliability. Although MOLCA measures consistency rather than absolute accuracy, its integration into ISO 19157-3 would advance data quality assessment, fostering transparency, interoperability, and confidence in HRLC-derived environmental analyses.

The goal of the CO3D (Constellation Optique 3D) mission is the full-automatic production of a worldwide accurate DSM (Digital Surface Model). This DSM is generated from stereo acquisitions obtained from a new generation of high-resolution optical satellites, called CO3D. The DSM accuracy is one meter in relative height for moderate slopes and four meters in absolute height with a one-meter grid space. Each of the four satellites of the constellation provides images with 0.50 m resolution in red, green, blue and near-infrared bands. The satellites resource is shared by, on one hand, the French institutions (government, scientists concerned by global Earth monitoring) who have dedicated access and preferred price conditions, and on the other hand, ADS (Airbus Defence and Space) customers interested in 2D and 3D products. The constellation was launched on July 26, 2025

We present the qualification of 3D products as part of the CO3D mission. The CO3D mission is dedicated to creating a digital surface model of the Earth's landmass cover. Massive automatic production is a challenge in itself, as this ground segment produces more advanced data for an optical mission. Firstly, the 3D product, which is generally retouched and checked, which can represent a significant cost. In the case of the CO3D mission, these products will be generated completely automatically. Masks will also be included to describe the processing history and provide precise information on the altitudes restored. All of this data requires detailed qualification with precise reference data and methods to best reflect its quality. The paper will describe all of these methods and data and provide an overview of the performance of these new CO3D products.

The launch of the four CO3D spacecrafts took place on 26th July 2025 aboard Vega-C from Kourou space center with Microcarb microsatellite. After a first week spent calibrating the most critical subsystems of the spacecraft, the instrument was switched on, enabling the 9-months Image Quality commissioning phase to begin. Images are acquired in RGB and NIR spectral bands with a 50 cm Ground Sampling Distance (GSD), thanks to matrix sensors based on a Bayer pattern. This brings new calibration challenges such as demosaicing and 2D line of sight determination. 3D calibration activities take place in a second stage of the commissioning phase once radiometric and geometric calibration are finalized.

This work introduces CO(ast)3D, a predictive pipeline that helps identify when upcoming CO3D satellite overpasses are likely to capture surface wave signals suitable for bathymetry acquisitions. Because CO3D cannot directly image the seafloor, depth must be inferred from the optical expression of surface waves, whose visibility depends strongly on illumination, viewing geometry, and sea state.

The pipeline combines CO3D orbital tracks with forecasted wave parameters from the Copernicus Marine model to construct a directional wave spectrum, generate a time-varying free surface through linear wave theory, and simulate CO3D-like radiance images at native spatial resolution. These synthetic scenes allow the clarity of the wave field to be evaluated a priori for any future time and location.

By predicting whether conditions will yield a sufficiently coherent wave signal, the system supports more efficient tasking, reduces acquisition risk, and improves the likelihood of capturing images suitable for accurate bathymetric inversion.

The challenges, main design elements, and results of the CODIP and ICC components will be presented in this paper, as well as the modalities for accessing and using the CO3D products.