AI for operational methane emitter monitoring from space
This system addresses the need for operational methane monitoring to combat global warming, providing actionable data for governments and stakeholders.
The authors tackled the problem of routine methane emission monitoring from space by developing MARS-S2L, an automated AI system for Sentinel-2 and Landsat imagery, which achieved a 216% improvement in mean average precision over a state-of-the-art method and yielded 457 near-real-time detections in 22 countries over six months.
Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise. Despite the demonstrated ability of remote sensing instruments to detect methane plumes, no system has been available to routinely monitor and act on these events. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery deployed operationally at the United Nations Environment Programme's International Methane Emissions Observatory. We compile a global dataset of thousands of super-emission events for training and evaluation, demonstrating that MARS-S2L can skillfully monitor emissions in a diverse range of regions globally, providing a 216% improvement in mean average precision over a current state-of-the-art detection method. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries of which 62 have already been used to provide formal notifications to governments and stakeholders.