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Detection of weak snow layer on skis using radar and machine learning


Description:

Off-piste skiing have grown tremendously in popularity, and 1 in 3 skiers and snowboarders now venture away from prepared slopes. This increases exposure to steep and avalanche-prone terrain, so the number of skiers who have been harmed by avalanches is steadily growing. Current avalanche products are designed for rescue rather than prevention, so skiers must rely on their own assessments of the snow and terrain conditions to avoid risky areas. This method is often inadequate and consequently, 9 of 10 skier involved avalanches are triggered by the skiers themselves. Very often a persistent weak snow layer, unseen by the skiers, causes the avalanche and too often, loss of human life. Knowledge of avalanche danger is essential for safe skiing, especially in remote, high mountain areas. Regional forecasting, topographic maps and weather reports offer avalanche risk indicators, but these are based on extrapolations from few data points, and do not offer accurate location specific information. Our project is the engineering of a portable, light-weight, low-cost radar solution combined with other sensors: GPS, gyro, temperature and tilt, for skiers in the backcountry. It is the first mass-market device for real-time data acquisition and analysis of the snow conditions. It digitally measures a full snow profile including weak layers and snow depth, as well as slope gradient and applied forces. This technology will keep skiers constantly informed about the snow conditions under them, no matter their location. This data is not only useful to the skier, but to ski resorts, hydropower plants, militaries, insurance companies, climate researchers and others. Our 1st milestone will be the automation of weak snow layer detection using machine learning. Then we will look into integrating other sources of data (weather, gyro ,etc..) to provide localized avalanche risk information and look into communicating it in such a way that it does not encourage more risk-taking behavior.


Project leader: Wayne Stasinowsky

Started: 2019

Ends: 2022

Category: Næringsliv

Sector: Næringsliv

Budget: 6405481

Institution: THINK OUTSIDE AS

Address: Bergen