Conference Agenda

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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