Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote sensing and computer vision, however, have the potential to augment compliance monitoring by detecting early warning signs of noncompliance. We demonstrate a process for rapid identification of significant structural expansion using Planet’s 3 m/pixel satellite imagery products and focusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a test case. Unpermitted building expansion has been a particular challenge with CAFOs, which pose significant health and environmental risks. Using new hand-labeled dataset of 145,053 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 = 0.86). A major advantage of this approach is that it can work with higher cadence (daily to weekly), but lower resolution (3 m/pixel), satellite imagery than previously used in similar environmental settings. It is also highly generalizable and thus provides a near real-time monitoring tool to prioritize enforcement resources in other settings where unpermitted construction poses environmental risk, e.g. zoning, habitat modification, or wetland protection.

美国的许多环境执法历来依赖于自我报告的数据或物理的、资源密集型的、不频繁的检查。然而，遥感和计算机视觉的进步有可能通过检测不合规的早期预警信号来加强合规监测。我们演示了使用 Planet 的 3 m/像素卫星图像产品快速识别显着结构扩张的过程，并以美国的集中动物饲养作业 (CAFO) 为测试案例。未经许可的建筑扩建一直是 CAFO 面临的一项特殊挑战，这会带来重大的健康和环境风险。使用新的手工标记数据集，其中包含 1,513 个 CAFO 的 145,053 张图像，我们将最先进的建筑分割与基于似然的变化点检测模型相结合，以提供稳健的建筑扩展信号 (AUC = 0.86)。这种方法的一个主要优点是它可以使用比以前在类似环境设置中使用的更高节奏（每天到每周）但分辨率更低（3 m/像素）的卫星图像。它也具有高度的通用性，因此提供了一种近乎实时的监控工具，可以在未经许可的建设带来环境风险的其他环境中优先执行执法资源，例如分区、栖息地改造或湿地保护。比以前在类似环境中使用的卫星图像。它也具有高度的通用性，因此提供了一种近乎实时的监控工具，可以在未经许可的建设带来环境风险的其他环境中优先执行执法资源，例如分区、栖息地改造或湿地保护。比以前在类似环境中使用的卫星图像。它也具有高度的通用性，因此提供了一种近乎实时的监控工具，可以在未经许可的建设带来环境风险的其他环境中优先执行执法资源，例如分区、栖息地改造或湿地保护。