  This paper introduces a new, highly consequential setting for the use of
computer vision for environmental sustainability. Concentrated Animal Feeding
Operations (CAFOs) (aka intensive livestock farms or "factory farms") produce
significant manure and pollution. Dumping manure in the winter months poses
significant environmental risks and violates environmental law in many states.
Yet the federal Environmental Protection Agency (EPA) and state agencies have
relied primarily on self-reporting to monitor such instances of "land
application." Our paper makes four contributions. First, we introduce the
environmental, policy, and agricultural setting of CAFOs and land application.
Second, we provide a new dataset of high-cadence (daily to weekly) 3m/pixel
satellite imagery from 2018-20 for 330 CAFOs in Wisconsin with hand labeled
instances of land application (n=57,697). Third, we develop an object detection
model to predict land application and a system to perform inference in near
real-time. We show that this system effectively appears to detect land
application (PR AUC = 0.93) and we uncover several outlier facilities which
appear to apply regularly and excessively. Last, we estimate the population
prevalence of land application events in Winter 2021/22. We show that the
prevalence of land application is much higher than what is self-reported by
facilities. The system can be used by environmental regulators and interest
groups, one of which piloted field visits based on this system this past
winter. Overall, our application demonstrates the potential for AI-based
computer vision systems to solve major problems in environmental compliance
with near-daily imagery.
