利用地理信息系统和街景图像进行热梯度分布审计

IF 6 2区 工程技术 Q1 ENVIRONMENTAL SCIENCES
Lang Zheng, Weisheng Lu, Jianxiang Huang, Fan Xue
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引用次数: 0

摘要

评估和管理城市街道景观中的热环境对大都市地区的健康、宜居性和生态可持续性至关重要。然而,由于用于预测和建模的高精度历史街道热环境数据稀缺,现有的城市热环境分级评估研究存在分辨率低(30 米)或研究范围有限(如社区级)的问题,导致得出的见解不够准确和全面。本研究引入了一个创新框架,利用从街道点的空间结构特征中提取的分类样本来构建大尺度城市街道级热梯度。该框架的核心在于 k-means 无监督分类算法。这种方法将详细的本地地理信息系统(GIS)数据与街景特征相结合,街景特征是通过使用 DeepLabV3 模型对谷歌街景全景进行语义分割计算得出的。这样就可以根据空间结构相似性对大量高精度街道点进行分类,而空间结构相似性是影响街道热环境特征相似性的关键因素。通过在每个类别中选择合适的样本进行现场热环境测量,然后将这些知识推断到每个类别的热环境分类中,该框架有助于在广泛的城市区域内快速创建高精度的街道级热梯度模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing geographic information system and street view imagery for thermal gradient distribution auditing
Assessing and managing the thermal environment within urban streetscapes is of paramount importance for the health, livability, and ecological sustainability of metropolitan regions. However, due to a scarcity of high-precision historical street thermal environment data for prediction and modeling, existing urban thermal environment classification assessment studies suffer from low resolution (> 30 m) or limited research scope (e.g., community-level), resulting in less accurate and comprehensive insights. This study introduces an innovative framework for constructing large-scale urban street-level thermal gradients using classified samples derived from the spatial structural features of street points. The core of this framework lies in the k-means unsupervised classification algorithm. This approach integrates detailed local geographic information system (GIS) data with street view features, calculated through semantic segmentation of Google Street-View-Panorama using the DeepLabV3 model. This allows for the categorization of a vast array of high-precision street points based on spatial structural similarity, a key factor influencing the similarity of street thermal environment features. By selecting appropriate samples for on-site thermal environment measurements within each category and subsequently extrapolating this knowledge to the thermal environment classification of each category, this framework facilitates the rapid creation of high-precision street-level thermal gradient models across extensive urban areas.
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来源期刊
Urban Climate
Urban Climate Social Sciences-Urban Studies
CiteScore
9.70
自引率
9.40%
发文量
286
期刊介绍: Urban Climate serves the scientific and decision making communities with the publication of research on theory, science and applications relevant to understanding urban climatic conditions and change in relation to their geography and to demographic, socioeconomic, institutional, technological and environmental dynamics and global change. Targeted towards both disciplinary and interdisciplinary audiences, this journal publishes original research papers, comprehensive review articles, book reviews, and short communications on topics including, but not limited to, the following: Urban meteorology and climate[...] Urban environmental pollution[...] Adaptation to global change[...] Urban economic and social issues[...] Research Approaches[...]
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