Deep Learning with Satellite Imagery to Enhance Environmental Enforcement

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Abstract/Contents

Abstract
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.

Description

Type of resource text
Date created 2021
Date modified August 10, 2021; December 5, 2022
Publication date June 7, 2021

Creators/Contributors

Author Handan-Nader, Cassandra
Author Ho, Daniel E.
Author Liu, Larry Y.

Subjects

Subject Deep Learning
Subject Environmental Enforcement
Subject Satellite Imagery
Subject Clean Air Act
Subject Clean Water Act
Subject Environmental Protection Agency
Subject Stanford Law School
Genre Text
Genre Book

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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.
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This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

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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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