Data scientists are using new techniques to identify lakes and reservoirs around the world
A team of data scientists led by the University of Minnesota’s Twin Cities has published the first comprehensive global dataset of lakes and reservoirs on Earth showing how they have changed over the past 30 years. The data will provide environmental researchers with new information about land and freshwater use and how lakes and reservoirs are affected by humans and climate change. This research is also a major advance in machine learning techniques. A paper highlighting the Reservoir and Lake Surface Area Timeseries (ReaLSAT) data set was recently published in Scientific Data, a peer-reviewed open access journal published by Nature. Study highlights include: The RealLSAT dataset contains location and surface area variations of 681,137 lakes and reservoirs greater than 0.1 square kilometers south of 50 degrees north latitude. The previous most comprehensive database, called HydroLAKES, has identified only 245,420 lakes and reservoirs for this part of the world and the minimum size c