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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2022
Research article |
| 02 Sep 2022
| 02 Sep 2022
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Elisabeth D. Hafner
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC¸ Davos Dorf, 7260, Switzerland
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
Patrick Barton
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
Rodrigo Caye Daudt
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
Jan Dirk Wegner
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
Institute for Computational Science, University of Zurich, Zurich, 8057, Switzerland
Konrad Schindler
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich, 8092, Switzerland
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC¸ Davos Dorf, 7260, Switzerland
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Cited
14 citations as recorded by crossref.
- Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery M. Kneib et al. 10.5194/tc-18-2809-2024
- AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection Z. Liu et al. 10.1088/1361-6501/ad7873
- Automated Snow Avalanche Monitoring and Alert System Using Distributed Acoustic Sensing in Norway A. Turquet et al. 10.3390/geohazards5040063
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. 10.5194/nhess-23-2895-2023
- Deep Learning-Based Glacial Lakes Extraction and Mapping in the Chandra–Bhaga Basin A. Sharma et al. 10.1007/s12524-024-01829-x
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. 10.1016/j.coldregions.2024.104179
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. 10.3390/atmos15111343
- Snow Avalanche Hazards and Avalanche-Prone Area Mapping in Tibet D. Chu et al. 10.3390/geosciences14120353
- Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations E. Hafner et al. 10.5194/tc-18-3807-2024
- Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work K. Kapper et al. 10.3389/frsen.2023.1156519
- Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation P. Keerthinathan et al. 10.1080/01431161.2023.2249604
- Detecting the impact of climate change on alpine mass movements in observational records from the European Alps M. Jacquemart et al. 10.1016/j.earscirev.2024.104886
- Assessing sandbar morphology in the Nakdong River Estuary using SPOT series satellite imagery S. Lee et al. 10.1080/1064119X.2024.2378074
- Automated avalanche hazard indication mapping on a statewide scale Y. Bühler et al. 10.5194/nhess-22-1825-2022
13 citations as recorded by crossref.
- Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery M. Kneib et al. 10.5194/tc-18-2809-2024
- AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection Z. Liu et al. 10.1088/1361-6501/ad7873
- Automated Snow Avalanche Monitoring and Alert System Using Distributed Acoustic Sensing in Norway A. Turquet et al. 10.3390/geohazards5040063
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. 10.5194/nhess-23-2895-2023
- Deep Learning-Based Glacial Lakes Extraction and Mapping in the Chandra–Bhaga Basin A. Sharma et al. 10.1007/s12524-024-01829-x
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. 10.1016/j.coldregions.2024.104179
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. 10.3390/atmos15111343
- Snow Avalanche Hazards and Avalanche-Prone Area Mapping in Tibet D. Chu et al. 10.3390/geosciences14120353
- Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations E. Hafner et al. 10.5194/tc-18-3807-2024
- Automated snow avalanche monitoring for Austria: State of the art and roadmap for future work K. Kapper et al. 10.3389/frsen.2023.1156519
- Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation P. Keerthinathan et al. 10.1080/01431161.2023.2249604
- Detecting the impact of climate change on alpine mass movements in observational records from the European Alps M. Jacquemart et al. 10.1016/j.earscirev.2024.104886
- Assessing sandbar morphology in the Nakdong River Estuary using SPOT series satellite imagery S. Lee et al. 10.1080/1064119X.2024.2378074
1 citations as recorded by crossref.
Latest update: 20 Jan 2025
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.
Knowing where avalanches occur is very important information for several disciplines, for...
