用光学遥感与空间模型结合，精准绘制加州戴维斯市树冠分布，助力城市热环境与规划决策。
From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California
- 通过深度学习与多方法融合，从高分辨率影像中提取树冠候选点并生成树冠表面。
- 覆盖29.8%城市面积的7.71平方公里树冠，精度达像素级0.804，与激光雷达产品高度一致。
- 输出可复现的树冠筛查图层，适合城市热岛研究、交通规划与生态评估者使用。
及时获取城市树冠信息对连接遥感数据与热环境、交通及社区规划至关重要。本研究基于2022年国家农业影像计划（0.6米分辨率RGB+NIR）影像，开发了一套光学地理人工智能工作流。DeepForest生成树冠候选点；结合NDVI阈值、非极大值抑制与盒提示的Segment Anything模型（ViT-B），构建了以树冠为中心的冠层表面。分析范围为25.92平方公里的戴维斯市行政区划边界，采用100米网格。该流程保留11,741个候选树冠，映射出7.71平方公里冠层（占城市总面积29.8%）。在相同范围内，像素精度为0.804，像素召回率为0.873；97.4%的候选中心与2022年美国农业部/加州消防局激光雷达辅助冠层产品匹配（交并比0.719，骰子系数0.837，面积恢复率108.5%）。约49%的树冠位于道路15米内。冠层与陆地卫星地表温度呈负相关（斯皮尔曼等级相关系数-0.477；控制建筑概率后的偏相关系数-0.551），空间滞后模型确认显著邻里结构。两个透明注意力图叠加了冠层需求与热力及上下文指标。该框架提供可重复、可更新的筛查图层，补充结构性冠层产品与市政清单，同时保留假设、数据溯源与空间诊断信息，便于规划解读。
原文摘要 · Abstract (English)
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 candidate centers agreed with the 2022 USDA/CAL FIRE LiDAR-assisted canopy product (IoU 0.719; Dice 0.837; area recovery 108.5%). Approximately 49% of candidates occurred within 15 m of a road. Canopy was inversely associated with Landsat land-surface temperature (Spearman rho = -0.477; partial rho = -0.551 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.
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