GlacierNet: A Deep-Learning Approach for Debris-Covered Glacier Mapping

Abstract

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number and size of layers and filters, and encoder depth based on the properties of the input data, CNN segmentation process and empirical results. The GlacierNet was then trained using input data and corresponding glacier boundaries from the Global Land Ice Measurements from Space (GLIMS) database, and tested on glaciers in the Karakoram and Nepal Himalaya. Our results show proof-of-concept that GlacierNet reasonably identifies the boundaries of DCGs with a relatively high degree of accuracy, and that morphometric parameters improves boundary detection.