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This paper introduces a new, highly consequential setting for the use ofcomputer vision for environmental sustainability. Concentrated Animal FeedingOperations (CAFOs) (aka intensive livestock farms or "factory farms") producesignificant manure and pollution. Dumping manure in the winter months posessignificant environmental risks and violates environmental law in many states.Yet the federal Environmental Protection Agency (EPA) and state agencies haverelied primarily on self-reporting to monitor such instances of "landapplication." Our paper makes four contributions. First, we introduce theenvironmental, policy, and agricultural setting of CAFOs and land application.Second, we provide a new dataset of high-cadence (daily to weekly) 3m/pixelsatellite imagery from 2018-20 for 330 CAFOs in Wisconsin with hand labeledinstances of land application (n=57,697). Third, we develop an object detectionmodel to predict land application and a system to perform inference in nearreal-time. We show that this system effectively appears to detect landapplication (PR AUC = 0.93) and we uncover several outlier facilities whichappear to apply regularly and excessively. Last, we estimate the populationprevalence of land application events in Winter 2021/22. We show that theprevalence of land application is much higher than what is self-reported byfacilities. The system can be used by environmental regulators and interestgroups, one of which piloted field visits based on this system this pastwinter. Overall, our application demonstrates the potential for AI-basedcomputer vision systems to solve major problems in environmental compliancewith near-daily imagery.

Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act