Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS)

Abstract. Large-scale monitoring is a crucial task for managing remote mountain environments, especially for hazardous events such as snow avalanches, debris flows or rockslides. One key information for safety-related applications is large-scale information on released avalanches. As avalanches occur in remote and potentially dangerous locations this data is difficult to obtain. Uncrewed fixed-wing aerial vehicles, due to their low cost, long range and high travel speeds are promising platforms to gather aerial imagery to map avalanche activity. However, autonomous flight in mountainous terrain remains a challenge due to the complex topography, regulations, and harsh weather conditions. In this work, we present a proof of concept system that is capable of safely navigating and mapping avalanches using a fixed-wing aerial system (UAS) and discuss the challenges arising for operating such a system. We show in our field experiments that we can effectively and safely navigate in steep mountain environments while maximizing the map quality and efficiency while meeting regulatory requirements. We expect our work to enable more autonomous operations of fixed-wing vehicles in alpine environments to maximize the quality of the data gathered. By enabling the acquisition of frequent and high quality information on avalanche activity, such drone systems would have a large impact of safety critical applications such as avalanche warning, mitigation measure planning or hazard mapping.