How to locate potentially polluting animal farms has long been a problem for environmental regulators. Now, Stanford scholars show how a map-reading algorithm could help regulators identify facilities more efficiently than ever before.
Law Professor Daniel Ho, along with PhD student Cassandra Handan-Nader, have figured out a way for machine learning – teaching a computer how to identify and analyze patterns in data – to efficiently locate industrial animal operations and help regulators determine each facility’s environmental risk. The researchers’ findings are set to publish April 8 in Nature Sustainability.
“Our work shows how a government agency can leverage rapid advances in computer vision to protect clean water more efficiently,” said Ho, the William Benjamin Scott and Luna M. Scott Professor of Law, and a senior fellow at the Stanford Institute for Economic Policy Research.
Co-author Cassandra Handan-Nader is a 2018 EDGE-SBEH Fellow.