Conference Agenda

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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