Can Avalanche Deposits be Effectively Detected by Deep Learning on Sentinel-1 Satellite SAR Images?

International audienceAchieving reliable observations of avalanche debris is crucial for many applications including avalanche forecasting. The ability to continuously monitor the avalanche activity, in space and time, would provide indicators on the potential instability of the snowpack and would allow a better characterization of avalanche risk periods and zones. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data and an independent in-situ avalanche inventory (ground truth) to automatically detect avalanche debris in the French Alps during the remarkable winter season 2017-18. Convolutional neural networks are applied on SAR image patches to locate avalanche debris signatures. We are able to successfully locate new avalanche deposits with as much as 77% confidence on the most susceptible mountain zone (compared to 53% with a baseline method). One of the challenges of this study is to make an efficient use of remote sensing measurements on a complex terrain. It explores the following questions: to what extent can deep learning methods improve the detection of avalanche deposits and help us to derive relevant avalanche activity statistics at different scales (in time and space) that could be useful for a large number of users (researchers, forecasters, government operators)

Detecting Avalanche Deposits using Variational Autoencoder on Sentinel-1 Satellite Imagery

International audienceAvalanche monitoring is a crucial safety challenge, especially in a changing climate. Remote sensing of avalanche deposits can be very useful to identify avalanche risk zones and time periods, which can in turn provide insights about the effects of climate change. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data on the French Alps for the exceptional winter of 2017-18, with the goal of automatically detecting avalanche deposits. We address our problem with an unsupervised learning technique. We treat an avalanche as a rare event, or an anomaly, and we learn a variational autoencoder, in order to isolate the anomaly. We then evaluate our method on labeled test data, using an independent in-situ avalanche inventory as ground truth. Our empirical results show that our unsupervised method obtains comparable performance to a recent supervised learning approach that trained a convolutional neural network on an artificially balanced version of the same SAR data set along with the corresponding ground-truth labels. Our unsupervised approach outperforms the standard CNN in terms of balanced accuracy (63% as compared to 55%). This is a significant improvement, as it allows our method to be used in-situ by climate scientists, where the data is always very unbalanced (< 2% positives). This is the first application of unsupervised deep learning to detect avalanche deposits

Can Avalanche Deposits be Effectively Detected by Deep Learning on Sentinel-1 Satellite SAR Images?

International audienceAchieving reliable observations of avalanche debris is crucial for many applications including avalanche forecasting. The ability to continuously monitor the avalanche activity, in space and time, would provide indicators on the potential instability of the snowpack and would allow a better characterization of avalanche risk periods and zones. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data and an independent in-situ avalanche inventory (ground truth) to automatically detect avalanche debris in the French Alps during the remarkable winter season 2017-18. Convolutional neural networks are applied on SAR image patches to locate avalanche debris signatures. We are able to successfully locate new avalanche deposits with as much as 77% confidence on the most susceptible mountain zone (compared to 53% with a baseline method). One of the challenges of this study is to make an efficient use of remote sensing measurements on a complex terrain. It explores the following questions: to what extent can deep learning methods improve the detection of avalanche deposits and help us to derive relevant avalanche activity statistics at different scales (in time and space) that could be useful for a large number of users (researchers, forecasters, government operators)