Contrail Detection

Contrail detection research focuses on automatically identifying condensation trails from aircraft exhaust in satellite imagery to assess their climate impact and inform mitigation strategies. Current efforts leverage deep learning, employing architectures like U-Net and EfficientNet, often enhanced with techniques such as transfer learning and novel loss functions designed to improve accuracy in challenging atmospheric conditions and address data scarcity. These advancements are crucial for developing robust contrail avoidance systems, enabling more precise estimations of aviation's contribution to climate change and facilitating the development of environmentally sustainable flight operations. Publicly available datasets and benchmark studies are fostering collaboration and accelerating progress in this field.