Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms

• URL: http://arxiv.org/abs/2105.14159v1
• Date: Sat, 29 May 2021 00:33:18 GMT
• Title: Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
• Authors: Ben Chugg, Brandon Anderson, Seiji Eicher, Sandy Lee, Daniel E. Ho
• Abstract summary: We demonstrate a process for rapid identification of significant structural expansion using satellite imagery. We combine state-of-the-art building segmentation with a likelihood-based change-point detection model to provide a robust signal of building expansion.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Environmental enforcement has historically relied on physical, resource-intensive, and infrequent inspections. Advances in remote sensing and computer vision have the potential to augment compliance monitoring, by providing early warning signals of permit violations. We demonstrate a process for rapid identification of significant structural expansion using satellite imagery and focusing on Concentrated Animal Feeding Operations (CAFOs) as a test case. Unpermitted expansion has been a particular challenge with CAFOs, which pose significant health and environmental risks. Using a new hand-labeled dataset of 175,736 images of 1,513 CAFOs, we combine state-of-the-art building segmentation with a likelihood-based change-point detection model to provide a robust signal of building expansion (AUC = 0.80). A major advantage of this approach is that it is able to work with high-cadence (daily to weekly), but lower resolution (3m/pixel), satellite imagery. It is also highly generalizable and thus provides a near real-time monitoring tool to prioritize enforcement resources to other settings where unpermitted construction poses environmental risk, e.g. zoning, habitat modification, or wetland protection.

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