A dual-branch contrastive learning framework distills street-view semantics and temporal context into satellite representations, improving monthly carbon emission prediction using only satellite imagery at inference.
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CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training
A dual-branch contrastive learning framework distills street-view semantics and temporal context into satellite representations, improving monthly carbon emission prediction using only satellite imagery at inference.