  Large-scale study of glaciers improves our understanding of global glacier
change and is imperative for monitoring the ecological environment, preventing
disasters, and studying the effects of global climate change. Glaciers in the
Hindu Kush Himalaya (HKH) are particularly interesting as the HKH is one of the
world's most sensitive regions for climate change. In this work, we: (1)
propose a modified version of the U-Net for large-scale, spatially
non-overlapping, clean glacial ice, and debris-covered glacial ice
segmentation; (2) introduce a novel self-learning boundary-aware loss to
improve debris-covered glacial ice segmentation performance; and (3) propose a
feature-wise saliency score to understand the contribution of each feature in
the multispectral Landsat 7 imagery for glacier mapping. Our results show that
the debris-covered glacial ice segmentation model trained using self-learning
boundary-aware loss outperformed the model trained using dice loss.
Furthermore, we conclude that red, shortwave infrared, and near-infrared bands
have the highest contribution toward debris-covered glacial ice segmentation
from Landsat 7 images.
