Project RISE: Recognizing Industrial Smoke Emissions


  • Yen-Chia Hsu Carnegie Mellon University
  • Ting-Hao (Kenneth) Huang Pennsylvania State University
  • Ting-Yao Hu Carnegie Mellon University
  • Paul Dille Carnegie Mellon University
  • Sean Prendi Carnegie Mellon University
  • Ryan Hoffman Carnegie Mellon University
  • Anastasia Tsuhlares Carnegie Mellon University
  • Jessica Pachuta Carnegie Mellon University
  • Randy Sargent Carnegie Mellon University
  • Illah Nourbakhsh Carnegie Mellon University



Environmental Sustainability


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 to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for Social Impact.




How to Cite

Hsu, Y.-C., Huang, T.-H. (Kenneth), Hu, T.-Y., Dille, P., Prendi, S., Hoffman, R., Tsuhlares, A., Pachuta, J., Sargent, R., & Nourbakhsh, I. (2021). Project RISE: Recognizing Industrial Smoke Emissions. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14813-14821.



AAAI Special Track on AI for Social Impact