发表机构
University of California, Davis; California High School（加利福尼亚大学戴维斯分校; 加利福尼亚高中）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究整合光学GeoAI与空间建模，利用2022年影像在戴维斯市生成树冠筛选层，绘制了该市9.37%的树冠，验证了其与热环境的关联，为城市规划提供支持。
AI 中文摘要
及时的城市树冠信息对于将遥感与热环境、流动性及邻域规划关联至关重要。我们针对加利福尼亚州戴维斯市开发了一套光学地理人工智能（GeoAI）工作流，使用2022年美国国家农业 imagery计划的影像（0.6米分辨率RGB+近红外波段）。DeepForest生成树冠候选；通过归一化植被指数（NDVI）阈值、非极大值抑制及框提示的Segment Anything Model（ViT-B）生成以树冠为锚点的冠层表面。分析使用了25.92平方公里的人口普查TIGER市政边界和100米网格。该工作流保留了11741个候选树冠，绘制了2.43平方公里的树冠，占该市面积的9.37%。在相同范围内，绘制的树冠像素的87.8%和候选中心的97.4%与2022年美国农业部（USDA）/加利福尼亚消防局（CAL FIRE）的激光雷达辅助树冠产品一致；该光学冠层表面占参考树冠面积的34.2%（交并比IoU为0.288；戴斯系数Dice为0.448）。约49%的候选树冠出现在距离道路15米范围内。树冠与陆地卫星（Landsat）地表温度呈负相关（斯皮尔曼相关系数rho=-0.293；控制建成概率后的偏相关系数rho=-0.370），空间滞后模型确认了清晰的邻域结构。两个透明注意力表面结合了树冠需求与热及 contextual指标。该框架提供了可重复、可更新的筛选层，补充结构性树冠产品和市政清单，同时保留假设、数据来源和空间诊断以用于规划解读。
英文摘要
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.
Comments17 pages, 10 figures + 5 supplementary figures, 7 tables. Revised preprint (expanded canopy infill; IoU 0.719). Preprint submitted to Taylor & Francis. Code: https://github.com/MohammadrezaNarimaniUCDavis/Davis_Urban_Canopy_GeoAI Data: https://doi.org/10.5281/zenodo.21925526
