Enforcement of environmental law depends critically on permitting and monitoring intensive animal agricultural facilities, known in the United States as ‘concentrated animal feeding operations’ (CAFOs). The current legal landscape in the United States has made it difficult for government agencies, environmental groups and the public to know where such facilities are located. Numerous groups have, as a result, conducted manual, resource-intensive enumerations based on maps or ground investigation to identify facilities. Here we show that applying a deep convolutional neural network to high-resolution satellite images offers an effective, highly accurate and lower cost approach to detecting CAFO locations. In North Carolina, the algorithm is able to detect 589 additional poultry CAFOs, representing an increase of 15% from the baseline that was detected through manual enumeration. We show how the approach scales over geography and time, and can inform compliance and monitoring priorities.
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The replication code and datasets generated during the current study are available in the GitHub repository github.com/slnader/cafo_public.
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We thank Z. Ashwood, G. Hong, C. Hull and A. Teuscher for research assistance, the Environmental Working Group for sharing data, Descartes Labs and Google Earth Engine for providing access to their research platform, the GRACE Communications Foundation and the Stanford Institute for Economic and Policy Research for generous support, and C. Cox, B. Erden, S. A. C. Gomez, M. Hancher, M. Engelman Lado, J. Lee, P. Lehner, D. Lobell, J. Quinlivan, S. Rundquist, D. Sivas and the participants of the Data for Sustainable Development class at Stanford University for helpful conversations.
The authors declare no competing interests.
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Handan-Nader, C., Ho, D.E. Deep learning to map concentrated animal feeding operations. Nat Sustain 2, 298–306 (2019). https://doi.org/10.1038/s41893-019-0246-x
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