Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens in pursuing environmental justice. However, existing datasets do not have sufficient quality nor quantity for training robust CV models to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopt the citizen science approach to collaborate with local community members in annotating whether a video clip has smoke emissions. Our dataset contains 12,567 clips with 19 distinct views from cameras on three sites that monitored three different industrial facilities. The clips are from 30 days that spans four seasons in two years in the daytime. We run experiments using deep neural networks developed for video action recognition to establish a performance baseline and reveal the challenges for smoke recognition. Our data analysis also shows opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.

我们引入了RISE，这是第一个用于识别工业烟雾排放的大规模视频数据集，它采用市民科学方法与当地社区协作以注释视频中是否有烟雾排放，数据集包含来自三种不同工业设施监测相机的19个视角的12,567个剪辑，并使用深度神经网络进行实验，以建立强大的性能基线并显示烟雾识别挑战。通过我们的调查研究，我们讨论了社区反馈，并显示了将市民科学家和人群工作者纳入人工智能应用于社会影响领域的机会。

Project RISE：识别工业烟气排放