A single multimodal transformer, Galileo, jointly learns global and local features from optical, radar, elevation, weather, and land-cover inputs and outperforms specialized models on eleven benchmarks.
Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
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abstract
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.
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Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
A single multimodal transformer, Galileo, jointly learns global and local features from optical, radar, elevation, weather, and land-cover inputs and outperforms specialized models on eleven benchmarks.