Automatic cloud detection in GHGSat satellite imagery

GHGSat operates a constellation of satellites that detect and quantify methane and carbon dioxide emissions from industrial facilities across the globe. With twelve satellites in orbit, each making around fifty observations per day, automatic data processing is required. A key step in the automation process is the detection of clouds. Identifying pixels that contain clouds or cloud shadow can improve the retrieval quality of cloudy observations and make it easier to detect greenhouse gas emissions. In this presentation, we discuss the ML/AI techniques used to detect and segment clouds in GHGSat imagery. We highlight some of the challenges encountered during the creation of training datasets and model training. A first guess at cloud masks is obtained with an unsupervised clustering approach to group pixels of similar intensity. Then, using a dataset of 1000 human-annotated observations, we compare the performance of U-NET and Mask2Former models trained for cloud segmentation. We discuss how the monitoring of training loss can help to identify problematic examples. Finally, we investigate the creation of cloud shadow masks using geometrical projections of the cloud masks, where cloud height is estimated through an intensity-based optimisation.