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In recent decades, climate change has significantly affected glacierdynamics, resulting in mass loss and an increased risk of glacier-relatedhazards including supraglacial and proglacial lake development, as well ascatastrophic outburst flooding. Rapidly changing conditions dictate the needfor continuous and detailed observations and analysis of climate-glacierdynamics. Thematic and quantitative information regarding glacier geometry isfundamental for understanding climate forcing and the sensitivity of glaciersto climate change, however, accurately mapping debris-cover glaciers (DCGs) isnotoriously difficult based upon the use of spectral information andconventional machine-learning techniques. The objective of this research is toimprove upon an earlier proposed deep-learning-based approach, GlacierNet,which was developed to exploit a convolutional neural-network segmentationmodel to accurately outline regional DCG ablation zones. Specifically, wedeveloped an enhanced GlacierNet2 architecture thatincorporates multiplemodels, automatic post-processing, and basin-level hydrological flow techniquesto improve the mapping of DCGs such that it includes both the ablation andaccumulation zones. Experimental evaluations demonstrate that GlacierNet2improves the estimation of the ablation zone and allows a high level ofintersection over union (IOU: 0.8839) score. The proposed architecture providescomplete glacier (both accumulation and ablation zone) outlines at regionalscales, with an overall IOU score of 0.8619. This is a crucial first step inautomating complete glacier mapping that can be used for accurate glaciermodeling or mass-balance analysis.

GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping