  In recent decades, climate change has significantly affected glacier
dynamics, resulting in mass loss and an increased risk of glacier-related
hazards including supraglacial and proglacial lake development, as well as
catastrophic outburst flooding. Rapidly changing conditions dictate the need
for continuous and detailed observations and analysis of climate-glacier
dynamics. Thematic and quantitative information regarding glacier geometry is
fundamental for understanding climate forcing and the sensitivity of glaciers
to climate change, however, accurately mapping debris-cover glaciers (DCGs) is
notoriously difficult based upon the use of spectral information and
conventional machine-learning techniques. The objective of this research is to
improve upon an earlier proposed deep-learning-based approach, GlacierNet,
which was developed to exploit a convolutional neural-network segmentation
model to accurately outline regional DCG ablation zones. Specifically, we
developed an enhanced GlacierNet2 architecture thatincorporates multiple
models, automatic post-processing, and basin-level hydrological flow techniques
to improve the mapping of DCGs such that it includes both the ablation and
accumulation zones. Experimental evaluations demonstrate that GlacierNet2
improves the estimation of the ablation zone and allows a high level of
intersection over union (IOU: 0.8839) score. The proposed architecture provides
complete glacier (both accumulation and ablation zone) outlines at regional
scales, with an overall IOU score of 0.8619. This is a crucial first step in
automating complete glacier mapping that can be used for accurate glacier
modeling or mass-balance analysis.
