AI & ML interests
We develop open-source machine-learning tools to detect, track and reduce aviation contrails and their climate impact. Our work covers contrail detection from both geostationary satellite imagery (GOES-16, MTG) and ground-based cameras, using semantic and instance segmentation. We also track contrails over time, attribute them to flights using ADS-B trajectories, and estimate their altitude. Beyond contrails, we build data-driven models of aviation's environmental impact, including emissions and fuel burn. Models and datasets are released openly to support research and operational contrail-avoidance studies.
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