The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 9
Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3517-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3517-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 9
Research article
 | 
02 Sep 2022
Research article |  | 02 Sep 2022

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

Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

Data sets

SPOT6 Avalanche outlines 24 January 2018 Elisabeth D. Hafner and Yves Bühler https://doi.org/10.16904/envidat.77

SPOT6 Avalanche outlines 16 January 2019 Elisabeth D. Hafner and Yves Bühler https://doi.org/10.16904/envidat.235

Model code and software

aval-e/DeepLab4Avalanches: Code to automatically identify avalanches in SPOT 6/7 imagery (v1.0.0) Patrick Barton and Elisabeth D. Hafner https://doi.org/10.5281/zenodo.7014498

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