Towards Automated Monitoring Of Glacial Lakes In Hindu Kush And Himalayas Using Deep Learning

Abstract

A glacial lake outburst flood (GLOF) is typically a natural phenomenon caused by rapid discharge of water from a glacier, leading to a flood. The frequency of GLOFs has increased significantly in the northern areas of Pakistan, which demands identification and continuous monitoring of potentially dangerous glacial lakes. In this paper, an up-to-date inventory of glacial lakes in this region is presented. This inventory (HKH-PK-2020) has been prepared using high resolution PlanetScope imagery acquired in 2020 over northern Pakistan. It contains a total of 8808 lakes. We compare our database with the High Mountain Asia (HMA) glacial lakes inventory over northern Pakistan, prepared in 2018 using Landsat imagery. The new inventory contains 6537 more glacial lakes than the HMA inventory. Furthermore, we have prepared an annotated dataset containing 3525 images (of high resolution PlanetScope imagery over a selected number of lakes from the inventory). Each image comprises 4 bands, namely red, green, blue, and near infrared. The annotations are binary: lake or background. Finally, we have performed an ablation study with two encoder-decoder based convolutional neural networks (CNNs) trained on this dataset for pixel-based classification. Our results show an intersection over union (IoU) score of 72.81% for the lake class, which is a promising first result indicating a use of deep learning for automated inventory updates in future.