| 摘要: |
| 在全球城市化和环境压力加剧的背景下，对城
市街道绿化泛类结构(urban street greening general
structure，USGGS)的量化是加强城市区域碳汇、缓解城市
热岛效应以应对全球气候变化的重要前提。通过量化与分析
不同城市的USGGS，探究其与城市建成环境之间的关系。使
用改进的DeepLabV3+神经网络模型，对天津、杭州、深圳
的城市全景街景图像进行语义分割，并结合细粒度数据量化
USGGS，使用Robust回归模型分析USGGS与城市功能属性
POI的关系。研究显示，天津的USGGS主要由单乔木和乔-灌
结构组成，与商业属性和生活属性的POI紧密相关；而杭州和
深圳则展现出包括草本植物在内的多样化USGGS与休闲文化
设施的POI更强的关联性。通过对3个城市USGGS的量化、分
析与比较，为城市绿色基础设施规划和管理奠定了一定的数据
基础，同时基于城市街景图像对USGGS的分析也为城市碳汇
计算与城市热环境研究提供了新的视角。 |
| 关键词: 风景园林 城市街道绿化泛类结构 街道空间 计
算机视觉 语义分割 Robust回归 |
| DOI：10.19775/j.cla.2024.09.0022 |
| 投稿时间：2024-05-06修订日期：2024-07-01 |
| 基金项目:国家自然科学基金重点项目(52038007) |
|
| Quantitative Analysis and Distribution Mechanismof Urban Street Greening General Structure Basedon Computer Vision and Street View Images |
| HU Yike,ZHANG Longhao,LIU Kaixin |
| Abstract: |
| In the context of global urbanization and increasing
environmental pressures, the quantification of Urban Street Greening
General Structure (USGGS) is an important prerequisite for enhancing
carbon sinks in urban areas and mitigating the urban heat island effect
in response to global climate change. By quantifying and analyzing the
USGGS in different cities, the relationship between the USGGS and the
built environment was explored. The improved DeepLabV3+ neural
network model is used to semantically segment the urban panoramic
street view images of Tianjin, Hangzhou, and Shenzhen, and combined
with the fine-grained data to quantify the USGGS, and the Robust
regression model is used to analyze the relationship between the
USGGS and the city's functional attributes of POIs. The study shows
that Tianjin's USGGS, which mainly consists of single trees and treeshrub
structures, is strongly correlated with the POIs of commercial
attributes and living attributes, while Hangzhou and Shenzhen show
a stronger correlation between diverse USGGS including herbaceous
plants and the POIs of leisure and cultural facilities. By quantifying,
analyzing and comparing the USGGS of the three cities, a certain
data foundation is laid for urban green infrastructure planning
and management, and the analysis of the USGGS based on urban
streetscape images also provides a new perspective for urban carbon
sink calculation and urban thermal environment research. |
| Key words: landscape architecture urban street greening general
structure street space computer vision semantic segmentation Robust Regression |
