# Project RISE: Recognizing Industrial Smoke Emissions

arXiv: https://arxiv.org/abs/2005.06111 · HTML: https://mlsanity.com/paper/2005.06111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 5 of 5)

- [Empowering Local Communities Using Artificial Intelligence](https://mlsanity.com/paper/2110.02007/) (2021)
- [SmokeBench: Evaluating Multimodal Large Language Models for Wildfire Smoke Detection](https://mlsanity.com/paper/2512.11215/) (2025)
- [FoSp: Focus and Separation Network for Early Smoke Segmentation](https://mlsanity.com/paper/2306.04474/) (2023)
- [Mitigating Spurious Correlations in Weakly Supervised Semantic Segmentation via Cross-architecture Consistency Regularization](https://mlsanity.com/paper/2507.21959/) (2025)
- [IJmond Industrial Smoke Segmentation Dataset](https://mlsanity.com/paper/2603.23754/) (2026)

## Abstract (arXiv)

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.