In this episode I caught up with Julia Wąsala to learn about methane plume detection using AutoML, and how her research bridges atmospheric science and machine learning. Julia explains the unique challenges of working with TROPOMI data—extremely coarse spatial resolution, single-channel methane measurements, and complex auxiliary fields that sometimes create plume-like artefacts leading to false detections. She walks through how her approach generalises a traditional two-stage detection pipeline to multiple gases using AutoMergeNet, a neural architecture search framework that automatically designs multimodal CNNs tailored to different atmospheric gases. We discuss why methane matters, how model performance shifts dramatically between curated test sets and real-world global data, and the ongoing effort to understand sampling bias and improve operational precision.
Bio: Julia Wąsala is currently working toward the Ph.D. degree in automated machine learning for Earth observation with the Leiden Institute for Advanced Computer Science, Leiden University, Leiden, The Netherlands, and with Space Research Organisation Netherlands, Leiden, The Netherlands. Her research focuses on the field of automated machine learning for earth observation focuses on designing new methods and validating them in real-world applications, such as atmospheric plume detection.
