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October 16, 2025Open Access

Monitoring snow avalanches from SAR data with deep learning

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Authors

FBFilippo Maria BianchiJGJakob Grahn

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Overview

This chapter reviews the application of deep learning for detecting and segmenting snow avalanches from SAR data, indicating significant improvements over traditional methods.

Key Points

  • Deep learning models significantly improve the detection and segmentation of snow avalanches.
  • Using Sentinel-1 SAR data, the study achieved higher accuracy compared to traditional monitoring methods.
  • The research demonstrated the effectiveness of pixel-level segmentation for better spatial resolution in avalanche detection.
  • An expanded dataset of over 4,500 annotated SAR images was used to optimize the model's performance.

Cite This Study

Bianchi et al. (2025) studied this question.

synapsesocial.com/papers/68f0d5eb105731330a2b2156https://doi.org/10.48550/arxiv.2502.18157
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  4. 4Weakly supervised learning for snow cover segmentation in mountainous areas from Sentinel-1 SAR images using interpolated NDSI time series2026
  5. 5Tracking Snow Avalanches: Integrating Field Observations and Satellite-Derived Indicators2026