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How machine learning can help environmental regulators

An algorithm that reads satellite images can help environmental regulators identify potentially hazardous agricultural facilities more efficiently than traditional approaches.

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.

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

Source : Stanford

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