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

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Cited articles

Abermann, J., Eckerstorfer, M., Malnes, E., and Hansen, B. U.: A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations, Nat. Hazards, 97, 517–534,, 2019. a, b
Barton, P. and Hafner, E. D.: aval-e/DeepLab4Avalanches: Code to automatically identify avalanches in SPOT 6/7 imagery (v1.0.0), Zenodo [code],, 2022. a
Bebi, P., Kulakowski, D., and Rixen, C.: Snow avalanche disturbances in forest ecosystems – State of research and implications for management, Forest Ecology Manag., 257, 1883–1892,, 2009. a
Bianchi, F. M., Grahn, J., Eckerstorfer, M., Malnes, E., and Vickers, H.: Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks, IEEE J. Sel. Top. Appl. Earth Obs., 14, 75–82,, 2021. a
Bründl, M. and Margreth, S.: Integrative Risk Management, in: Snow and Ice-Related Hazards, edited by: Haeberli, W. and Whiteman, C., Risks Disast., 2015, 263–301,, 2015. a
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.