CS&E Data Scientists Create First-of-its-kind Lakes and Reservoirs Dataset
The ReaLSAT dataset is a comprehensive global dataset of lakes and reservoirs, tracking changes over the last 30+ years


Data scientists from the Department of Computer Science and Engineering at the University of Minnesota Twin Cities led an interdisciplinary research team to publish a comprehensive global dataset of lakes and reservoirs, tracking changes over the last 30+ years. Supported by the U.S. National Science Foundation and NASA, this first-of-it-kind dataset will inform environmental researchers how lakes and reservoirs are impacted by climate change.

Vipin Kumar, Regents Professor and William Norris Endowed Chair in the CS&E Department, was the senior author of the study that highlights the Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset. Marking a major milestone of the eight-year project, the ReaLSAT dataset represents the possibilities of applying knowledge-guided machine learning to environmental sciences.

The research was supported by the U.S. National Science Foundation Awards 1029711, 1838159, 1934633, and NASA grant NNX12AP37G. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.

Read the full story on the College of Science and Engineering website, or check out this feature on the Natural Science Foundation (NSF) website.
Learn more about the ReaLSAT dataset on the Scientific Data website.

Contacts:

Rhonda Zurn, College of Science and Engineering, rzurn@umn.edu

University Public Relations, unews@umn.edu

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