Researchers have developed a new optical GeoAI workflow to assess urban tree canopy cover in Davis, California. This method utilizes high-resolution imagery and deep learning models like DeepForest and Segment Anything Model (SAM) to identify and map individual tree crowns and overall canopy surface. The workflow successfully mapped 9.37% of the city's canopy, with high agreement with existing LiDAR-assisted products, and revealed an inverse relationship between canopy cover and land surface temperature, highlighting the importance of urban greenery for mitigating heat. AI
IMPACT This GeoAI approach offers a reproducible method for urban planning and heat island mitigation strategies.
RANK_REASON The cluster contains an academic paper detailing a new methodology and its application. [lever_c_demoted from research: ic=1 ai=1.0]
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