Conference Agenda

Capacity building is a key element in promoting and training in spatial technologies and in fostering a network of early-stage researchers for future collaborations. The first edition of the Earth Sensing Summer School, organised by the University of Padova with support from ISPRS Education and Capacity Building (ECBI) funds, was held from 7 to 13 September 2025 in the Dolomite area of the Alpine region. This article illustrates specific aspects of the organisation and discusses the return on investment in terms of training and networking. It highlights the methodology used for selecting participants and conducting the training, which included a balanced combination of seminars, fieldwork, data analysis, dissemination to peers, and a final defence of results. We discuss the outcomes and feedback from the almost 40 participants and provide ideas for future improvements, aiming to offer insights for fellow researchers who might want to replicate a capacity-building activity of this kind.

This practice paper presents the design, implementation, and first evaluation of Team-Based Learning (TBL) activities in university-level Geomatics courses taught in English to multicultural and international student groups. The study documents a structured pathway for adapting TBL to technically demanding subjects, including GIS suitability analysis, network analysis, remote sensing classification, and heat-risk assessment. Its main contribution lies in showing how a pedagogical model widely discussed in general higher education can be translated into software-based Geomatics teaching while supporting both disciplinary learning and intercultural collaboration. The paper also identifies the main organizational conditions for successful adoption, including team formation, workload calibration, and suitable classroom settings. Results from 12 TBL implementations involving 187 students and 470 total participations show clear benefits of teamwork: average team test performance was markedly higher than individual performance, repeated participation was associated with improved results, and student satisfaction increased after the introduction of TBL. Qualitative evidence further indicates gains in communication, teamwork, and intercultural interaction. Although the first implementation required substantial preparation effort, the approach proved replicable and scalable in subsequent editions, making TBL an effective instructional model for Geomatics education.

The ISPRS Student Consortium (SC) Summer Schools are one of the fundamental initiatives that ISPRS SC jointly organises with interested institutions to advance education outreach and capacity building in photogrammetry, remote sensing, and geospatial information sciences. Since their start in 2004, these programs have provided students and young researchers with immersive learning opportunities, combining technical lectures, hands-on sessions, and cultural experiences. Grounded in Experiential Learning Theory, the Summer Schools emphasise real-world application, reflective observation, and collaboration. This paper explores their evolution, global outreach, and educational impacts. Drawing on recent ISPRS SC Summer Schools, including the BUCEA Summer School 2024 on Smart Cities and the Summer School 2024 on AI for Geospatial Applications, the analysis highlights their integration of theory and practice, networking benefits, and transformative cultural exchange. Challenges such as financial barriers and technological gaps are discussed together with recommendations for sustaining and enhancing these initiatives. This study underscores the critical role of ISPRS SC Summer Schools in fostering a global community of geospatial practitioners to address real-world challenges.

This paper discusses the importance of integrating legal aspects into UAS mapping courses and related curricula providing a framework for integration in an introductory photogrammetric course with sample questions and assignments. Curricula focus is placed on two interrelated issues: first, the extent to which property owners maintain a reasonable expectation of privacy from UAS intrusion within the “immediate reaches” of their airspace; and second, the potential for UAS-mounted sensors to inadvertently capture imagery or point cloud of neighboring properties while operating in compliance with Federal Aviation Administration (FAA) regulations. The discussion concludes by identifying strategies to mitigate these legal and operational challenges, giving students the knowledge and tools to address similar situations in real-life scenarios, and ensuring that aerial surveying practices respect both regulatory compliance and property rights.

Remote sensing (RS) and especially earth observation (EO) have been used extensively for decades in environmental monitoring, infrastructure asset management, urban planning, emergency response, mapping and many others. The pace of technology

advancements in big data, cloud computing, Geospatial AI (GeoAI) and Geospatial Foundation Models (GeoFM) causes a paradigm shift on how to and who can maximize the potential of remote sensing technology. This paradigm shift challenges traditional geomatics education and pedagogical methods. Additionally, the gap between geomatics graduates’ skills and market needs is widening. The pace of disruptive technology advances like GeoAI and GeoFM often outpaces developments in geomatics education content or suitable pedagogical methods and formats. To address these skills gaps in geomatics courses and courseware, an initiative has been developed

between the Canadian Space Agency and Carleton University, involving more than a dozen different partners spanning industry, government, academia and NGOs. We have been gathering information through qualitative and quantitative techniques to obtain

insights about the soft and hard skills that are valuable and/or lacking in contemporary geomatics graduates, to forecast trends and future needs, and plan how to optimize the introduction of new technology and techniques into the educational content. Based on the mapped feedback, university-level geomatics courses are being redeveloped and updated, and novel course modules, mini-courses and micro credential programs are being developed and tested.

The WG Africa project is a collaborative initiative bringing together 12 national institutions from 8 European countries. Its objective is to support and strengthen the use of Copernicus data and services in Africa through a training-of-trainers program funded by the European Commission under the Framework Partnership Agreement on Copernicus User Uptake (FPCUP) and implemented in French, Portuguese, and English. To widely support the Copernicus products uptake, the primary goal is to collaborate with African academic and private-sector trainers by integrating Copernicus-based modules into their training programs or curricula. This initiative complements other capacity-building efforts in space-based Earth Observation in Africa, such as GMES & Africa and the Global Gateway European initiative.

Geospatial education in the UK is facing a critical decline, despite the increasing relevance of 3D reality capture and spatial technologies across sectors. While industry recognises the value of geospatial skills, the absence of coordinated national policy or incentives has led to the closure of key undergraduate programmes. Notably, Newcastle University closed its geospatial UG programme in 2023. The University of East London remains the only UK institution offering a dedicated undergraduate surveying degree, supplemented by an industry-linked apprenticeship. To address the skills gap, several universities now offer postgraduate conversion courses in geospatial science, primarily within geography or environmental science departments.

Outreach has emerged as a vital strategy to raise awareness and inspire future talent. GeospatialUK.org, developed at Newcastle University with industry support, provides accessible resources on careers, study pathways, and classroom activities aligned with UK education curricula at high school level. Its exercises—ranging from mapping hazards, wildfires, census data to GNSS-based calculations—bridge advanced research with school-level learning. It also offers insight into geospatial relate careers and links to possible job opportunities. The platform has gained international traction and continues to attract users.

This paper highlights the urgent need for national coordination in geospatial education and showcases GeospatialUK.org as a scalable model for outreach. Without intervention, the UK risks a shortage of skilled geospatial professionals, undermining its capacity to address pressing societal challenges

3D Gaussian Splatting (3DGS) has revolutionized real-time rendering with state-of-the-art novel view synthesis, but its applicability to accurate geometric measurement remains limited. Compared with multi-view stereo (MVS)-based point clouds or mesh models, 3DGS provides superior visual quality and completeness, while existing measurement approaches still rely on stereoscopic workstations or direct measurements on incomplete and inaccurate reconstructed geometry.

As a novel view synthesizer, 3DGS reproduces source views and smoothly interpolates intermediate viewpoints, enabling users to intuitively identify congruent points across multiple views. By triangulating these correspondences, accurate 3D point measurements can be obtained. Inspired by traditional stereoscopic measurement, the proposed approach removes the need for stereo workstations and biological stereoscopic capability, while naturally supporting multi-view measurements for improved accuracy.

We implement a web-based application to demonstrate this proof of concept using UAV-based aerial datasets. Experimental results show that the proposed method achieves measurement accuracy comparable to or better than traditional stereoscopic measurement approaches while operating entirely on non-stereo workstations. In particular, the proposed method consistently outperforms direct mesh-based measurements, achieving RMSEs of 1–2 cm on well-defined points. On challenging thin structures, the proposed method reduces RMSE from 0.062 m to 0.037 m, and successfully measures sharp corners where mesh-based methods fail entirely.

The source code and documentation are open-source and available at: https://github.com/GDAOSU/3dgs_measurement_tool.

Traditional 3D Gaussian Splatting (3DGS) methods initialize Gaussian primitives from Structure-from-Motion point clouds, resulting in loosely distributed representations that lack geometric constraints and metric accuracy. This limitation severely restricts their application in architectural heritage preservation, where millimeter-level precision and practical editability are essential requirements.

This paper introduces Gaussian Texturing, a novel framework that fundamentally transforms how Gaussians relate to geometry by directly binding 3D Gaussian primitives to precisely measured mesh surfaces—essentially "texturing" surfaces with Gaussians. Our approach comprises three key innovations: (1) a constrained optimization framework that maintains tight Gaussian-surface coupling throughout training, preventing geometric drift while preserving photorealistic rendering quality; (2) engineering-oriented editing tools enabling geometry-based material replacement, region editing, and mesh-driven deformation; and (3) seamless integration with professional heritage preservation workflows.

Experimental validation on MipNeRF360 benchmarks and custom architectural datasets demonstrates that our method achieves millimeter-level geometric precision while maintaining competitive rendering metrics. Unlike traditional "bind-after-training" approaches, our direct surface binding paradigm eliminates intermediate reconstruction steps, ensuring accuracy from source data. Real-world applications in heritage documentation and architectural design confirm the method's practical value, successfully bridging the gap between photorealistic visualization and engineering-grade geometric accuracy for professional applications.

In this study, we develop a Structured framework for Gaussian Splatting (3DGS) with LiDAR integration (Structured-Li-GS). It is a lightweight Gaussian Splatting pipeline that leverages LiDAR–inertial–visual SLAM. Structured-Li-GS achieves high-quality 3D reconstructions with fewer Gaussians by training on accurate, dense, colorized point clouds. Gaussian primitives are anchored using sub-sampled point clouds, and their ellipsoidal parameters are initialized from local surface geometry. Our training strategy integrates a comprehensive set of loss terms, including photometric, flattening, offset, depth, and normal losses—guided by the dense point cloud, enabling accurate reconstruction without Gaussian densification. This approach produces up-to-scale, high-fidelity results with a moderate model size. For experimental validation, we develop a custom hardware-synchronized LiDAR–camera handheld scanner. Experiments on both benchmark datasets and our real-world in-house dataset demonstrate that Structured-Li-GS surpasses state-of-the-art methods while using fewer Gaussians.

True Digital Orthophoto Maps (TDOMs) are essential for urban analysis and map updating, traditionally generated through photogrammetric workflows involving aerial triangulation, DSM construction, and orthorectification. Recently, 3D Gaussian Splatting (3DGS) has emerged as an alternative approach using differentiable volumetric rendering. While both methods depend on acquisition geometry, they follow fundamentally different reconstruction processes, potentially producing distinct representational characteristics. Systematic comparisons under controlled conditions remain limited.

This study generates photogrammetric and 3DGS-based TDOMs from four UAV datasets acquired over the same area with varying resolution (2.51–5.8 cm GSD), image count, and oblique view proportion (0–75%). All datasets were preprocessed through common SfM to obtain identical inputs. We evaluate differences through inter-method agreement (PSNR, SSIM, LPIPS), detail preservation (gradient magnitude, high-frequency energy), and spatial distribution patterns (boundary–interior separation).

Results show 3DGS systematically smooths fine-scale texture with gradient ratios of 0.58–0.89 and high-frequency energy reductions of 2.5–55× relative to photogrammetry. Oblique view proportion emerges as the dominant divergence factor: oblique-dominant datasets show lowest agreement (PSNR 15.15) despite larger image counts, while nadir-only datasets achieve higher similarity (PSNR 26.73). Difference maps reveal 2–3 times higher discrepancies along boundaries than interiors. Visually cleaner 3DGS boundaries are byproducts of overall smoothing rather than superior reconstruction. These findings establish that the two methods are complementary—photogrammetry preserving texture fidelity and 3DGS providing structural regularity—with acquisition geometry critically influencing performance characteristics.

Efficient and robust 3D scene representation is crucial in fields such as robotics, autonomous driving, and augmented reality. While RGBimagesprovidevaluable content for 3D reconstruction, other modalities like thermal or depth can enable additional information on the 3D environment. Lately, Novel View Synthesis (NVS) methods like Gaussian Splatting (GS) have started using multiple modalities to further boost their performance. But fusing or combining those multi-modal data can make the process slower and bring in additional challenges. Therefore, our project aims to use single modality based on thermal infrared domain, by removing the reliance on visible light, as much as possible. We propose a method Thermal-to-Depth Gaussian (TDg), that uses only thermal images and depth estimation in its architecture to derive the radiance fields. Mainstream methods relying heavily on RGB images, perform poorly in visually degraded environments, such as low-light conditions, fog, smoke, or extreme weather. Contrary to this, infrared cameras can detect objects’ inherent thermal radiation and provide a robust perception, suitable regardless of lighting and weather conditions. But despite their promise, thermal images are inherently characterized by low contrast, sparse texture, and non-uniform brightness distribution. So current approaches still rely heavily on paired RGB images for supervision or joint optimization, failing to establish a truly independent and purely thermal-based Gaussian representation system. Therefore, the core innovation of our work is to prepare a self contained Thermal GS framework that uses only thermal image inputs. We design a thermal-guided depth estimation module, Thermal-to-Depth (TDg), providing explicit and reliable constraints for geometric optimization.