Conference Agenda

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.