用深度学习从卫星雷达图自动识别雪崩范围，精度超80%。
Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection
- 仅用灾前灾后雷达图像，端到端检测雪崩变化区域。
- 在保守配置下F1达0.806，召回优化时命中率80.36%。
- 公开多区域标注数据集，助力雪崩监测研究。
准确的遥感影像变化检测对监测雪崩等快速地质灾害至关重要，这类灾害因频率和强度上升正日益威胁人类生命、基础设施与生态系统。本研究系统性地利用哨兵-1合成孔径雷达（Sentinel-1 SAR）影像，通过双时相变化检测实现大范围雪崩制图。在多个高山生态区的实验中，基于人工验证的雪崩清单显示，将任务视为仅依赖灾前灾后SAR图像的单模态变化检测，表现最稳定。所提端到端流程在保守（F1优化）配置下取得0.8061的F1分数，在较宽松、侧重召回（F2优化）的调参下，达到0.8414的F2分数，并实现80.36%的雪崩多边形命中率。结果揭示了精确率与完整性的权衡关系，表明阈值调整有助于发现更小或边缘性雪崩。该研究发布的多区域标注数据集，为基于SAR的雪崩制图提供了可复现的基准。
原文摘要 · Abstract (English)
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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