Using AI to Accelerate the Smart Green Transition
summary
Claims a production-ready deep vision engine that detects rooftops, segments and classifies them into flat / heavy industrial / low-clutter / existing solar, estimates available area and predicts solar yield as a single energy variable, and cuts site-survey time from hours or days to automated outputs. Pipeline uses annotated imagery with a 70/30 train/test split, segmentation via Mask R-CNN, area extraction, material via k-means and color with CIE76, and returns prioritized installation sites and ROI/carbon estimates.
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chapters (6)
- 0:00AI for green energy transition
- 2:34Rooftop solar adoption challenges
- 3:58AI for rooftop solar potential
- 5:02ML pipeline for solar mapping
- 7:28Solution architecture and classification
- 10:36AI insights for energy sectors
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