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
19 citations as recorded by crossref.
- Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery M. Kneib et al. https://doi.org/10.5194/tc-18-2809-2024
- Avalanche debris detection from Sentinel-2 data using fuzzy machine learning and colour spaces for the Indian Himalaya A. Abhinav et al. https://doi.org/10.1080/2150704X.2025.2488532
- Snow avalanche susceptibility, hazard, and exposure assessment in the Western Himalaya using machine learning and numerical modelling A. Abhinav & A. Sattar https://doi.org/10.1038/s41598-025-22051-w
- AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection Z. Liu et al. https://doi.org/10.1088/1361-6501/ad7873
- Automated Snow Avalanche Monitoring and Alert System Using Distributed Acoustic Sensing in Norway A. Turquet et al. https://doi.org/10.3390/geohazards5040063
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. https://doi.org/10.5194/nhess-23-2895-2023
- Enhanced detection and inventory of snow avalanches along the Leh–Manali Highway, Western Himalayas using remote sensing: Development and demonstration of the modified SAFE (mSAFE) algorithm R. Chandra Prabha et al. https://doi.org/10.1016/j.rsase.2025.101830
- Deep Learning-Based Glacial Lakes Extraction and Mapping in the Chandra–Bhaga Basin A. Sharma et al. https://doi.org/10.1007/s12524-024-01829-x
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. https://doi.org/10.1016/j.coldregions.2024.104179
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. https://doi.org/10.3390/atmos15111343
- Snow Avalanche Hazards and Avalanche-Prone Area Mapping in Tibet D. Chu et al. https://doi.org/10.3390/geosciences14120353
- Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland F. Techel et al. https://doi.org/10.5194/nhess-25-3333-2025
- Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations E. Hafner et al. https://doi.org/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. https://doi.org/10.3389/frsen.2023.1156519
- Towards slope-scale assessment of avalanche formation: Exploring UAV-borne GPR for unveiling spatial snowpack variability A. Siebenbrunner et al. https://doi.org/10.1016/j.coldregions.2025.104741
- Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation P. Keerthinathan et al. https://doi.org/10.1080/01431161.2023.2249604
- Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS) J. Lim et al. https://doi.org/10.5194/nhess-26-411-2026
- Detecting the impact of climate change on alpine mass movements in observational records from the European Alps M. Jacquemart et al. https://doi.org/10.1016/j.earscirev.2024.104886
- Assessing sandbar morphology in the Nakdong River Estuary using SPOT series satellite imagery S. Lee et al. https://doi.org/10.1080/1064119X.2024.2378074
19 citations as recorded by crossref.
- Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery M. Kneib et al. https://doi.org/10.5194/tc-18-2809-2024
- Avalanche debris detection from Sentinel-2 data using fuzzy machine learning and colour spaces for the Indian Himalaya A. Abhinav et al. https://doi.org/10.1080/2150704X.2025.2488532
- Snow avalanche susceptibility, hazard, and exposure assessment in the Western Himalaya using machine learning and numerical modelling A. Abhinav & A. Sattar https://doi.org/10.1038/s41598-025-22051-w
- AVA-YOLO: image-based multiscale feature fusion enhanced perception model for snow avalanche detection Z. Liu et al. https://doi.org/10.1088/1361-6501/ad7873
- Automated Snow Avalanche Monitoring and Alert System Using Distributed Acoustic Sensing in Norway A. Turquet et al. https://doi.org/10.3390/geohazards5040063
- Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. https://doi.org/10.5194/nhess-23-2895-2023
- Enhanced detection and inventory of snow avalanches along the Leh–Manali Highway, Western Himalayas using remote sensing: Development and demonstration of the modified SAFE (mSAFE) algorithm R. Chandra Prabha et al. https://doi.org/10.1016/j.rsase.2025.101830
- Deep Learning-Based Glacial Lakes Extraction and Mapping in the Chandra–Bhaga Basin A. Sharma et al. https://doi.org/10.1007/s12524-024-01829-x
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. https://doi.org/10.1016/j.coldregions.2024.104179
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. https://doi.org/10.3390/atmos15111343
- Snow Avalanche Hazards and Avalanche-Prone Area Mapping in Tibet D. Chu et al. https://doi.org/10.3390/geosciences14120353
- Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland F. Techel et al. https://doi.org/10.5194/nhess-25-3333-2025
- Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations E. Hafner et al. https://doi.org/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. https://doi.org/10.3389/frsen.2023.1156519
- Towards slope-scale assessment of avalanche formation: Exploring UAV-borne GPR for unveiling spatial snowpack variability A. Siebenbrunner et al. https://doi.org/10.1016/j.coldregions.2025.104741
- Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation P. Keerthinathan et al. https://doi.org/10.1080/01431161.2023.2249604
- Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS) J. Lim et al. https://doi.org/10.5194/nhess-26-411-2026
- Detecting the impact of climate change on alpine mass movements in observational records from the European Alps M. Jacquemart et al. https://doi.org/10.1016/j.earscirev.2024.104886
- Assessing sandbar morphology in the Nakdong River Estuary using SPOT series satellite imagery S. Lee et al. https://doi.org/10.1080/1064119X.2024.2378074
Saved (final revised paper)
Latest update: 30 May 2026
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...
