# Monitoring snow avalanches from SAR data with deep learning

arXiv: https://arxiv.org/abs/2502.18157 · HTML: https://mlsanity.com/paper/2502.18157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 22 of 22)

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- [Large-scale detection and categorization of oil spills from SAR images with deep learning](https://mlsanity.com/paper/2006.13575/) (2020)
- [Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1910.05411/) (2019)

## Abstract (arXiv)

Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility, weather conditions, and cost. Satellite-borne Synthetic Aperture Radar (SAR) data has become an important tool for large-scale avalanche detection, as it can capture data in all weather conditions and across remote areas. However, traditional processing methods struggle with the complexity and variability of avalanches. This chapter reviews the application of deep learning for detecting and segmenting snow avalanches from SAR data. Early efforts focused on the binary classification of SAR images, while recent advances have enabled pixel-level segmentation, providing greater accuracy and spatial resolution. A case study using Sentinel-1 SAR data demonstrates the effectiveness of deep learning models for avalanche segmentation, achieving superior results over traditional methods. We also present an extension of this work, testing recent state-of-the-art segmentation architectures on an expanded dataset of over 4,500 annotated SAR images. The best-performing model among those tested was applied for large-scale avalanche detection across the whole of Norway, revealing important spatial and temporal patterns over several winter seasons.