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.
Enhancing and interpreting deep learning for sea ice charting using the autoice benchmark.Remote Sensing Applications: Society and Environment, 38:101538
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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.