Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps

• Published in: Remote Sensing
• Main Authors: Ingmar Nitze, Konrad Heidler, Sophia Barth, Guido Grosse
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: deep learning; image segmentation; permafrost thaw; semantic segmentation; disturbances; computer vision; Science; Q; Arctic; Canada; Herschel Island; Tuktoyaktuk; Banks Island; Kolguev; lena river; permafrost
• Online Access: https://doi.org/10.3390/rs13214294; https://doaj.org/article/b13024976aac4c3ebe8ec61149dc2bb5

In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions.