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.

翻译：雪崩对人类生命和基础设施构成重大风险，尤其是在山区，这使得有效监测至关重要。传统的监测方法，如实地观测，受限于可及性、天气条件和成本。星载合成孔径雷达（SAR）数据已成为大规模雪崩检测的重要工具，因为它能够在全天候条件下获取偏远地区的数据。然而，传统处理方法难以应对雪崩的复杂性和多变性。本章综述了利用深度学习从SAR数据中检测和分割雪崩的应用。早期研究集中于SAR图像的二元分类，而近期的进展则实现了像素级分割，提供了更高的精度和空间分辨率。一项使用Sentinel-1 SAR数据的案例研究证明了深度学习模型在雪崩分割中的有效性，其性能优于传统方法。我们还介绍了此项工作的一个扩展，即在包含超过4,500张标注SAR图像的扩展数据集上测试了近期最先进的分割架构。测试中表现最佳的模型被应用于挪威全境的大规模雪崩检测，揭示了多个冬季的重要时空分布规律。