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Stanford scholars show how machine learning can help environmental monitoring and enforcement

heat map images of swine and poultry facilities
Image credit: National Agriculture Imagery Program / U.S. Department of Agriculture
Apr 8 2019
Fellow, Research, Stanford

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.

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