Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Kathrin Maier, Philipp Bernhard, Sophia Ly, Michele Volpi, Ingmar Nitze, Shiyi Li, Irena Hajnsek

Abstract: Highlights•We developed a deep learning method to detect RTS activity on differential DEMs.•The method achieved a segmentation IoU of 0.58, mean IoU of 0.73, and F1 of 0.75.•The automatic segmentation method matches domain experts’ RTS labelling accuracy.•Model predictions of RTS area and volume change have an accuracy of ±20%.•We identified 4882 active Arctic RTSs between 2010 and 2021 across 71 400 km2.