🤖 AI Summary
This study addresses the challenge of outdated urban canopy data by proposing an optical GeoAI framework integrating DeepForest and SAM to assess tree canopy coverage and its thermal effects in Davis, California. The method generates transparent attention surfaces that ensure both data traceability and spatial diagnostics. Experimental mapping of 2.43 km² achieved a 97.4% centroid agreement with LiDAR references, confirming a significant negative correlation between canopy cover and land surface temperature while revealing community-level spatial structures. This research provides urban planning with a high-precision, reproducible screening layer that effectively complements existing remote sensing products, offering a robust solution for timely urban environmental monitoring and heat mitigation strategies.
📝 Abstract
Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optical GeoAI workflow for Davis, California, using 2022 National Agriculture Imagery Program imagery (0.6 m RGB+NIR). DeepForest generated crown candidates; an NDVI threshold, non-maximum suppression, and box-prompted Segment Anything Model (ViT-B) produced a crown-anchored canopy surface. Analyses used the 25.92 km2 Census TIGER municipal boundary and a 100 m grid. The workflow retained 11,741 candidate crowns and mapped 2.43 km2 of canopy (9.37% of the city). On the identical extent, 87.8% of mapped canopy pixels and 97.4% of candidate centers agreed with the 2022 USDA/CAL FIRE LiDAR-assisted canopy product; the optical surface represented 34.2% of the reference canopy area (IoU 0.288; Dice 0.448). Approximately 49% of candidates occurred within 15 m of a road. Canopy was inversely associated with Landsat land-surface temperature (Spearman rho = -0.293; partial rho = -0.370 controlling for built probability), and spatial-lag modeling confirmed clear neighborhood structure. Two transparent attention surfaces combined canopy need with thermal and contextual indicators. The framework provides a reproducible, updateable screening layer that complements structural canopy products and municipal inventories while retaining assumptions, data provenance, and spatial diagnostics for planning interpretation.