Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2025Galileo: Learning Global & Local Features of Many Remote Sensing Modalitiescited by 43
- 2022GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mappingcited by 6
- 2021Interactive Visualization and Representation Analysis Applied to Glacier Segmentationcited by 1
- 2023Boundary Aware U-Net for Glacier Segmentationcited by 1
- 2025Galileo: Learning Global & Local Features of Many Remote Sensing Modalities13 Feb 2025
- 2023Boundary Aware U-Net for Glacier Segmentation26 Jan 2023
- 2022GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping6 Apr 2022
- 2021Interactive Visualization and Representation Analysis Applied to Glacier Segmentation11 Dec 2021