A proxy consistency loss trains location encoders on proxy geographic data to outperform direct input fusion or frozen embeddings for air quality and poverty mapping with sparse labels.
Using multiple in- put modalities can improve data-efficiency and OOD generalization for ML with satellite imagery
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A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders
A proxy consistency loss trains location encoders on proxy geographic data to outperform direct input fusion or frozen embeddings for air quality and poverty mapping with sparse labels.