  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 improve monitoring.
However, the current state-of-the-art detection algorithms, based on radar
signal processing techniques, are still much less accurate than 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 6,345
manually labelled avalanches from 117 Sentinel-1 images, each one consisting of
six channels that include backscatter and topographical information. Then, we
tested our trained model on a new 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 sits at an F1 score of only 38\%. A visual
inspection of the results generated by our deep learning model shows that only
small avalanches are undetected, while some avalanches that were originally not
labelled by the human expert are discovered.
