Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks

Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to overcome this monitoring problem. Current state-of-the-art detection algorithms, based on radar signal processing techniques, have highly varying accuracy that is on average much lower than the accuracy of visual detections from human experts. To reduce this gap, we propose a deep learning architecture for detecting avalanches in Sentinel-1 radar images. We trained a neural network on 6345 manually labelled avalanches from 117 Sentinel-1 images, each one consisting of six channels with backscatter and topographical information. Then, we tested the best network configuration on one additional SAR image. Comparing to the manual labelling (the gold standard), we achieved an F1 score above 66%, while the state-of-the-art detection algorithm produced an F1 score of 38%. A visual interpretation of the network's results shows that it only fails to detect small avalanches, while it manages to detect some that were not labelled by the human expert.