Automating avalanche detection in ground-based photographs with deep learning

Automated avalanche detection has previously relied on satellite imagery, which is typically unsuitable for real-time monitoring due to long revisit times. To address this, we propose automating avalanche detection using photographs taken from the ground. This paper introduces image classification and avalanche segmentation tasks on a publicly-released dataset of 4090 photographs annotated by experts. Using the ResNet and YOLO architectures, we achieve avalanche detection F1 scores of 94.4% per image and 65.4% per avalanche region, demonstrating the potential that this method offers for avalanche monitoring. In contrast with existing approaches, we label images by avalanche type into four distinct categories: glide, loose-snow, slab, and no avalanche. This labelling scheme provides more detail on avalanche events than binary labels and is shown to improve model F1 scores. Moreover, our models do not require a digital elevation model, simplifying application to new areas. Trained models can be used for real-time avalanche monitoring and to gather temporally continuous data for the improvement of existing avalanche forecasting models. The code and dataset are available at github.com/j-f-ox/avalanche-detection.