Automatic Detection and Segmentation of Methane Plumes in GHGSat Imagery 

GHGSat currently operates 14 methane satellites and has plans to expand the constellation further, acquiring images globally of facilities that could emit methane for monitoring and mitigation. The constellation produces almost 1,000 observations per day in which methane is detected, geolocated and quantified. It is impractical to rely on human inspection alone at such large scale, and so automated solutions are required. However, automation must handle a highly complex classification problem—distinguishing small methane plumes from retrieval artifacts—while operating reliably at very high throughput. Two common types of automation that help human operators are using machine learning models to detect methane and to propose segmentation masks. The first one helps reduce the total amount of data seen by human operators, and the second helps reduce the operator time spent per observation. While these types of automation are common in methane detection with coarse-resolution public satellites such as Sentinel-2 or EMIT, their applications to fine spectral and spatial resolution satellites have been more limited. To handle the growing amount of data, we develop transformer-based detection and segmentation models, which can assist operators in processing the observations. We present the models used and performance achieved in terms of precision and recall, both for detection and segmentation, as well as discuss future improvements to further diminish operator time.