Deep Snow: Deep Learning for Snow Depth Monitoring
Reliable information on the spatial distribution of snow in mountain ranges are critical for risk assessment, outdoor activities, and water resource management. These water resources from snowmelt are indispensable as they provide drinking water, supply crop production and generate hydroelectric power worldwide. Despite this importance, we lack an accurate and operational spatiotemporal quantification of transient water storage in mountain ranges. Consequently, accurate estimates of snow quantities in space and time are the most important unsolved problem in mountain hydrology.
In this project, we aim to develop novel snow products for Switzerland based on multiple Earth Observation (EO) datasets and deep learning algorithms. Our objective is to provide seamless, timely information on snow cover, snow depth and snow water equivalent (SWE) on a daily basis in a high spatial resolution (20 m pixel spacing) via multiple data services. Successful completion would outperform current standards (weekly information with 1 km pixel spacing) and enable new market opportunities. These include improved outdoor safety standards, hydropower production planning and real-time snow risk assessment among others.
Our team is uniquely poised to meet this challenge, consisting of members from EcoVision, ExoLabs, and WSL with expertise in snow monitoring, remote sensing and deep learning. Together, we propose an ambitious, though realistic, project plan to generate, validate, and deploy snow products and services by harnessing the power of EO with deep learning using scalable cloud computing. These products would be really valuable for water management and tourism safety, and could help us better understand how the snow in the alps is reacting to climate change and evolving over time.
Project Partners:
ExoLabs
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)
Contacts:
Jan Dirk Wegner, ETH Zurich,
Rodrigo Caye Daudt, ETH Zurich,
Hendrik Wulf, ExoLabs,
Yves Bühler, WSL,