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arXiv · 2603.22658

Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection

Mattia Gatti·Alberto Mariani·Ignazio Gallo·Fabiano Monti
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Abstract

Accurate change detection from satellite imagery is essential for monitoring rapid mass-movement hazards such as snow avalanches, which increasingly threaten human life, infrastructure, and ecosystems due to their rising frequency and intensity. This study presents a systematic investigation of large-scale avalanche mapping through bi-temporal change detection using Sentinel-1 synthetic aperture radar (SAR) imagery. Extensive experiments across multiple alpine ecoregions with manually validated avalanche inventories show that treating the task as a unimodal change detection problem, relying solely on pre- and post-event SAR images, achieves the most consistent performance. The proposed end-to-end pipeline achieves an F1-score of 0.8061 in a conservative (F1-optimized) configuration and attains an F2-score of 0.8414 with 80.36% avalanche-polygon hit rate under a less conservative, recall-oriented (F2-optimized) tuning. These results highlight the trade-off between precision and completeness and demonstrate how threshold adjustment can improve the detection of smaller or marginal avalanches. The release of the annotated multi-region dataset establishes a reproducible benchmark for SAR-based avalanche mapping.

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Mattia Gatti, Alberto Mariani, Ignazio Gallo, Fabiano Monti. 2026-03-24. Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection. https://arxiv.org/abs/2603.22658

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