20 Apr 2022
20 Apr 2022
Status: this preprint is currently under review for the journal TC.

Automated avalanche mapping from SPOT 6/7 satellite imagery: results, evaluation, potential and limitations

Elisabeth D. Hafner1,2,3, Patrick Barton3, Rodrigo Caye Daudt3, Jan Dirk Wegner3,4, Konrad Schindler3, and Yves Bühler1,2 Elisabeth D. Hafner et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
  • 2Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC¸Davos Dorf, 7260, Switzerland
  • 3EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
  • 4Institute for Computational Science, University of Zurich, Zurich, 8057, Switzerland

Abstract. Spatially dense and continuous information on avalanche occurrences is crucial for numerous safety related applications such as avalanche warning, hazard zoning, hazard mitigation measures, forestry, risk management and numerical simulations. This information is today still collected in a non-systematic way by observers in the field. Current research has explored and proposed applying remote sensing technology to fill this information gap by providing spatially continuous information on avalanche occurrences over large regions. Previous investigations have confirmed the high potential of avalanche mapping from remote sensed imagery to complement existing databases. Currently, the bottleneck for fast data provision from optical data is the time- consuming manual mapping. In our study we deploy a slightly adapted DeepLabV3+, a state-of-the-art deep learning model, to automatically identify and map avalanches in SPOT6/7 imagery from 24 January 2018 and 16 January 2019. We relied on 24'778 manually annotated avalanche polygons split into geographically disjoint regions for training, validating and testing. Additionally, we investigate generalization ability by testing our best model configuration on SPOT 6/7 data from 6 January 2018 and comparing to avalanches we manually annotated for that purpose. To assess the quality of the model results, we investigate the probability of detection (POD), the positive predictive value (PPV) and the F1-score. Additionally, we assessed the reproducibility of manually annotated avalanches in a small subset of our data. We achieved an average POD of 0.610, PPV of 0.668 and an F1-score of 0.625 in our test areas and found an F1-score in the same range for avalanche outlines annotated by different experts. Our model and approach are an important step towards a fast and comprehensive documentation of avalanche periods from optical satellite imagery in the future, complementing existing avalanche databases. This will have a large impact on safety related applications, making mountain regions safer.

Elisabeth D. Hafner et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-80', Ron Simenhois, 11 May 2022
    • AC1: 'Reply on RC1/ Ron Simenhois', Elisabeth D. Hafner, 31 May 2022
  • RC2: 'Comment on tc-2022-80', Ron Simenhois, 13 May 2022
    • AC2: 'Reply on RC2', Elisabeth D. Hafner, 17 Jun 2022
  • RC3: 'Comment on tc-2022-80', Edward Bair, 09 Jun 2022
    • AC3: 'Reply on RC3/ Edward Bair', Elisabeth D. Hafner, 21 Jun 2022

Elisabeth D. Hafner et al.

Elisabeth D. Hafner et al.


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Short summary
Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation or risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automize the time- consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally good as the experts in identifying avalanches.