Deep Learning with Satellite Imagery to Enhance Environmental Enforcement
- The protection of air, water, and land depends critically on the role of government agencies that monitor and enforce environmental laws. In the United States, the Environmental Protection Agency (EPA) administers a vast range of statutory schemes, with regulations touching on critical industries, including energy, agriculture, transportation, and construction. Notwithstanding landmark statutes, such as the Clean Air Act and the Clean Water Act, there is increasing evidence that regulatory bodies struggle in enforcing these laws. We argue that the vast increase in the quantity and quality of satellite imagery, coupled with rapid advances in computer vision, often dubbed the “deep learning” revolution, has the potential to substantially enhance environmental monitoring and enforcement.
|Type of resource
|August 10, 2021; December 5, 2022
|June 7, 2021
|Ho, Daniel E.
|Liu, Larry Y.
|Clean Air Act
|Clean Water Act
|Environmental Protection Agency
|Stanford Law School
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
- Preferred citation
- Cassandra Handan-Nader, Daniel E. Ho & Larry Y. Liu, Deep Learning with Satellite Imagery to Enhance Environmental Enforcement, in DATA SCIENCE APPLIED TO SUSTAINABILITY ANALYSIS 206-28 (Jennifer Dunn & Prasanna Balaprakash eds., Elsevier, 2021). Stanford Digital Repository. Available at: https://purl.stanford.edu/bh005pt4088
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