Mapping Glacial Lakes Using Historically Guided Segmentation Models - Ortiz, Anthony; Tian, Weiyushi; Sherpa, Tenzing; Shrestha, Finu; Matin, Mir; Dodhia, Rahul; Lavista Ferres, Juan M.; Sankaran, Kris (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022)
In this article, we compare several approaches to segmenting glacial lakes in the Hindu Kush Himalayas in order to support glacial lake area mapping. More automatic mapping could support risk assessments of Glacial Lake Outburst Floods, a type of natural hazard that poses a risk to communities and infrastructure in valleys below glacial lakes. We propose and evaluate several approaches that incorporate labels from a 2015 survey using Landsat 7 ETM+ SLC-off imagery to guide segmentation on newer higher resolution satellite images such as Sentinel 2 and Bing Maps imagery, comparing them also to approaches that do not use this form of weak prior. We find that a guided version of U-Net and a properly initialized form of morphological snakes are most effective for these two datasets, respectively, each providing between an 8–10% intersection-over-union improvement over existing U-Net segmentation approaches. An error analysis highlights the strengths and limitations of each method. We design visualizations to support the discovery of lakes of potential concern, including an interactive exploratory interface. All the codes supporting our study are released in public repositories.