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

TL;DR

This paper introduces a deep learning-based change detection method using Sentinel-1 SAR images for large-scale avalanche mapping, achieving high accuracy and establishing a benchmark dataset for future research.

Contribution

It presents a novel end-to-end pipeline for avalanche detection from SAR imagery, demonstrating effective performance and providing a publicly available annotated dataset.

Findings

F1-score of 0.8061 in conservative detection mode

F2-score of 0.8414 with recall-oriented tuning

Threshold adjustment improves detection of smaller avalanches

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.