Machine Learning for the Detection of Arctic Melt Ponds from Infrared Imagery (Papers Track)
Marlena Reil (University of Osnabrück and University of Bremen, Institute of Environmental Physics); Gunnar Spreen (University of Bremen, Institute of Environmental Physics); Marcus Huntemann (University of Bremen, Institute of Environmental Physics); Lena Buth (Alfred Wegener Institute); Dennis Wilson (University of Toulouse, ISAE-Supaero)
Abstract
Melt ponds are pools of water on Arctic summer sea ice that play an important role in the Arctic climate system. Retrieving their coverage is essential to better understand and predict the rapidly changing Arctic, but current data are limited. The goal of this study is to enhance melt pond data by developing a method that segments thermal infrared (TIR) airborne imagery into melt pond, sea ice, and ocean classes. Due to temporally and spatially varying surface temperatures, we use a data-driven deep learning approach to solve this task. We adapt and fine-tune AutoSAM, a Segment Anything-based segmentation model. We make the code, data, and models available online.