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


Interactive discussion

Status: closed

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

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (23 Jun 2022) by Kang Yang
AR by Elisabeth D. Hafner on behalf of the Authors (05 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Jul 2022) by Kang Yang
RR by Ron Simenhois (16 Jul 2022)
ED: Publish as is (23 Jul 2022) by Kang Yang
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.