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.
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
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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.