Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset

Abstract

Glacial lakes, crucial components of the cryosphere, are recognized as key sentinels of climate change. While satellite imagery offers a straightforward method for monitoring their dynamics, traditional approaches are often subjective and time-consuming. Deep learning techniques, though promising, have been hindered by the scarcity of labeled glacial lake datasets. To address this limitation, we present the Glacial Lake Image Dataset (GLID), the first publicly available collection of its kind. This dataset comprises 18,367 (512 × 512 pixels) sample pairs (lake polygons and corresponding images) derived from 36 scenes from across multiple sources (WorldView-2, Sentinel-2, Landsat-8, and Gaofen-2), covering the entire Himalayan region. We then propose a transferable deep learning network for glacial lake extraction. Our findings underscore the critical role of high-quality training data in model performance. The GLID-trained model achieved superior results, demonstrating a Precision of 95.36 %, Recall of 87.50 %, F1 score of 91.66 %, and mIoU of 82.07 %. Notably, this method exhibits promising transferability across diverse regions, including North America, South America, Greenland, and High Mountain Asia. The GLID dataset provides a valuable resource for advancing machine learning-based glacial mapping research. By offering a large-scale, publicly accessible collection of labeled data, we aim to facilitate the development of more accurate and efficient methods for monitoring and understanding the impacts of climate change on glacial lake ecosystems.