Artificial intelligence for methane detection: from continuous monitoring to verified mitigation
This addresses the problem of reducing potent greenhouse gas emissions for environmental stakeholders by targeting large point sources, representing a scalable but incremental improvement in detection technology.
The paper tackles the challenge of detecting and attributing large methane emissions at scale by introducing MARS-S2L, a machine learning model that identifies methane plumes in satellite imagery, achieving 78% detection with an 8% false positive rate at 697 sites and enabling verified mitigation of six persistent emitters.
Methane is a potent greenhouse gas, responsible for roughly 30\% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creating an opportunity for substantial reductions by targeting relatively few sites. Detection and attribution of large emissions at scale for notification to asset owners remains challenging. Here, we introduce MARS-S2L, a machine learning model that detects methane emissions in publicly available multispectral satellite imagery. Trained on a manually curated dataset of over 80,000 images, the model provides high-resolution detections every two days, enabling facility-level attribution and identifying 78\% of plumes with an 8\% false positive rate at 697 previously unseen sites. Deployed operationally, MARS-S2L has issued 1,015 notifications to stakeholders in 20 countries, enabling verified, permanent mitigation of six persistent emitters, including a previously unknown site in Libya. These results demonstrate a scalable pathway from satellite detection to quantifiable methane mitigation.