Decadal Evolution of Retrogressive Thaw Slumps Retrieved from Landsat Imagery Via Heatmap Regression: A Case Study of the Beiluhe Region in Central Tibet

Retrogressive thaw slumps (RTSs) have become more common in the Circum-Arctic and the Qinghai-Tibet Plateau. Due to the longevity of the RTSs, it is necessary to conduct observation for more than ten years. With decades of continuous service, Landsat can potentially be used to serve the purpose, but the low resolution of its images can be challenging for delineating such a relatively small landform. Thus, designing an algorithm that can capture the development of RTSs from Landsat images is key to understanding the long-term spatial-temporal evolution of RTSs over a large extent. In this paper, we propose a heatmap regression-based deep learning method to locate and estimate the area of RTSs. Our method is applied to the Beiluhe study area using Landsat 8 images from 2013 to 2022 and the results reveal the abrupt increase of RTSs after 2016 and the estimated areas match well with the ground truth data.