Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work

Frontiers in Remote Sensing

Remote Sensing

Alexander Prokop ¹  Kathrin Lisa Kapper²  Jakob Abermann ²  Wolfgang Schöner ²  Christoph Gaisberger ²  Birgit Schlager ³  Stefan Muckenhuber ³  Thomas Goelles ³  Andreas Trügler ⁴  Eirik Malnes ⁵  Jakob Grahn ⁵  Markus Eckerstorfer ⁶ 

[1] Department of Geology, University of Vienna, Vienna, Austria;Snow Scan GmbH, Vienna, Austria;Institute of Geography and Regional Science, University of Graz, Graz, Austria;Institute of Geography and Regional Science, University of Graz, Graz, Austria;E/E & Software, Virtual Vehicle Research GmbH, Graz, Austria;Institute of Geography and Regional Science, University of Graz, Graz, Austria;Know-Center GmbH, Graz, Austria;Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria;NORCE Norwegian Research Centre AS, Tromsø, Norway;Norwegian Water Resources and Energy Directorate, Oslo, Norway;

关键词: remote sensing;    synthetic aperture radar;    machine learning;    sentinel-1;    Austrian alps;    U-net;    snow avalanches;   

DOI  :  10.3389/frsen.2023.1156519

 received in 2023-02-01, accepted in 2023-03-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

Avalanches pose a significant threat to the population and infrastructure of mountainous regions. The mapping and documentation of avalanches in Austria is mostly done by experts during field observations and covers usually only specific localized areas. A comprehensive mapping of avalanches is, however, crucial for the work of local avalanche commissions as well as avalanche warning services to assess, e.g., the avalanche danger. Over the past decade, mapping avalanches from satellite imagery has proven to be a promising and rapid approach to monitor avalanche activity in specific regions. Several recent avalanche detection approaches use deep learning-based algorithms to improve detection rates compared to traditional segmentation algorithms. Building on the success of these deep learning-based approaches, we present the first steps to build a modular data pipeline to map historical avalanche cycles in Copernicus Sentinel-1 imagery of the Austrian Alps. The Sentinel-1 mission has provided free all-weather synthetic aperture radar data since 2014, which has proven suitable for avalanche mapping in a Norwegian test area. In addition, we present a roadmap for setting up a segmentation algorithm, in which a general U-Net approach will serve as a baseline and will be compared with the mapping results of additional algorithms initially applied to autonomous driving. We propose to train the U-Net using labeled training dataset of avalanche outlines from Switzerland, Norway and Greenland. Due to the lack of training and validation data from Austria, we plan to compile the first avalanche archive for Austria. Meteorological variables, e.g., precipitation or wind, are highly important for the release of avalanches. In a completely new approach, we will therefore consider weather station data or outputs of numerical weather models in the learning-based algorithm to improve the detection performance. The mapping results in Austria will be complemented with pointwise field measurements of the MOLISENS platform and the RIEGL VZ-6000 terrestrial laser scanner.

【 授权许可】

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Copyright © 2023 Kapper, Goelles , Muckenhuber , Trügler , Abermann , Schlager , Gaisberger , Eckerstorfer , Grahn , Malnes , Prokop  and Schöner .