From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California
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 7.71 km2 of canopy (29.8% of the city). On the identical extent, pixel precision was 0.804, pixel recall was 0.873, and 97.4% of candi...
| Authors | Mohammadreza Narimani, Shreyan Mitra, Parastoo Farajpoor |
|---|---|
| Primary category | eess.IV |
| Published | 14 August 2026 |
| Last updated at source | 31 August 2026 |
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