| 04 Jan 2026
Automated UAV systems for geohazard monitoring: case studies from the Supphellebreen icefall (Norway), the Skjøld instability (Norway), and the Blatten landslide (Switzerland)
Abstract. This study presents the first systematic field evaluation of dock-based UAV (Uncrewed Aerial Vehicle) systems for geohazard monitoring in mountainous terrain. We tested their potential across three different environments: (1) a fast-moving glacier icefall (Supphellebreen, Norway), (2) an unstable rock slope (Skjøld, Norway), and (3) a post-failure landscape resulting from a catastrophic rock-ice avalanche (Blatten, Switzerland). Effective hazard management requires timely detection of displacement patterns and terrain change. To address these issues, we introduce an automated workflow integrating multitemporal UAV dock data acquisition with an end-to-end processing pipeline for displacement field generation and change detection. The results show that this workflow has the potential to provide data at centimetre-level accuracy before, during, and after hazard events, supporting both precautionary risk assessments and timely decision-making in critical phases of potential hazard evolution. Wider adoption will depend on supportive regulatory frameworks, reliable power and communication infrastructure, and sufficient expertise to ensure effective operation, maintenance, data interpretation and risk management. Overall, dock-based UAV systems represent a significant technological advancement in efficient geohazard monitoring, facilitating rapid response in critical situations, thereby contributing to increased resilience of communities living in vulnerable mountain environments.
Competing interests: Ueli Sager is CEO of the company Remote Vision, which is developer of Skylens in collaboration with the company FLARM
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Summary and overall assessment
The paper presents the first systematic field evaluation of dock-based UAV systems for geohazard monitoring across three alpine environments and introduces an automated end-to-end workflow for displacement and change detection. The topic is timely and important; however, several sections remain too general, and the case-study specific results and methodological details need expansion to demonstrate what was concretely achieved and learned at each site
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