PDF(Pubmed)
Abstract：
Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents\' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators.
摘要：
如果城市社区的绿地不足以营造舒适的环境，就会出现城市热岛，居民的健康将受到不利影响。当前的卫星图像只能有效地识别大规模的绿色空间，无法捕获三维建筑空间内的行道树或盆栽植物。在这项研究中,我们使用Google街景上的深度卷积神经网络语义分割模型来提取台北市邻里级的环境特征，台湾,包括绿色植被指数(GVI)，建筑视图因子,和天空景观因素。使用空间分辨率为0.01°的2018年至2021年的每月温度数据。我们应用了线性混合效应模型和地理加权回归来探索行人绿色空间与环境温度之间的关联，控制季节，土地利用信息,和交通量。他们的结果表明，较高的GVI与较低的环境温度和温差显着相关。交通流量或特定土地用途较高的地点，如宗教或政府，与更高的环境温度有关。总之,社区一级街景图像的GVI可以提高对城市绿地的理解，并与其他社会和环境指标一起评估其影响。
