Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
This work addresses the problem of accelerating glacier mapping for ecological monitoring in the Hindu Kush Himalaya, which is critical for individuals whose livelihoods depend on these ecosystems.
This paper presents a machine learning approach for semi-automated mapping of glaciers, including both clean ice and debris-covered types, from satellite imagery in the Hindu Kush Himalaya region. The authors developed a model to identify and outline glaciers and also released data and a web tool for visualization and correction of predictions.
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.