# Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

arXiv: https://arxiv.org/abs/2012.05013 · HTML: https://mlsanity.com/paper/2012.05013/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Galileo: Learning Global & Local Features of Many Remote Sensing Modalities](https://mlsanity.com/paper/2502.09356/) (2025)
- [GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping](https://mlsanity.com/paper/2204.05818/) (2022)
- [Interactive Visualization and Representation Analysis Applied to Glacier Segmentation](https://mlsanity.com/paper/2112.08184/) (2021)
- [Boundary Aware U-Net for Glacier Segmentation](https://mlsanity.com/paper/2301.11454/) (2023)

## Abstract (arXiv)

Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process.