CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training
arXiv:2607.07292v1 Announce Type: cross Abstract: Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing data. We propose CarbonCLIP, a task-oriented multimodal distillation framework that improves satellite-based carbon emission prediction by transferring contextual knowledge into a unified satellite representation through dual-branch contrastive learning. Unlike conventiona
The increasing availability of diverse geospatial data and advances in multimodal AI models enable more refined and cross-modal urban analysis.
Accurate, fine-grained urban carbon mapping is crucial for effective climate policy, sustainable development, and resource allocation in rapidly urbanizing regions.
The ability to integrate varied urban data sources (satellite, street-view) into a unified AI framework significantly enhances the fidelity and consistency of carbon emission prediction.
- · Urban planners
- · Climate scientists
- · Smart city initiatives
- · Geospatial AI companies
- · Cities with inefficient carbon monitoring
- · Legacy carbon accounting methods
Improved urban carbon emission databases will lead to more targeted environmental regulations and infrastructure investments.
This enhanced data could enable real-time carbon footprint monitoring for individual city districts or assets, driving behavioral and policy changes.
The methodology could be extended to predict other urban environmental factors, creating a comprehensive digital twin for environmental management.
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Read at arXiv cs.AI