基于街景图像的“三生空间”识别方法研究  

"Production-Living-Ecological Spaces"Recognition Methods based on Street View Images

作　　者：万江琴 费腾[1] WAN Jiangqin;FEI Teng(School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学资源与环境科学学院,武汉430079

出　　处：《地球信息科学学报》2023年第4期838-851,共14页Journal of Geo-information Science

基　　金：国家自然科学基金面上项目(42271476);武汉大学351人才计划(2020007)。

摘　　要：从生产-生活-生态功能角度划分城市空间格局,不仅是国土空间结构优化的基础,也能反映城市用地的内在功能形态和空间组合模式。然而,以往城市“三生空间”的识别主要利用遥感图像、兴趣点和土地利用数据进行,缺少城市内部的立体信息。街景图像可以体现城市内部街道特征,实现街道内部物理环境近距离大规模高分辨率的客观测量。因此,本文基于街景图像提取场景语义特征,提出一种中心城区“三生空间”的识别和特征重要性分析的方法。以成都市四环内为研究区,使用梯度极限提升算法识别城市“三生空间”,进行模型精度对比检验,从道路、格网和交通分析区3个尺度分析研究区“三生空间”的格局分布特征,并引入沙普利加和解释(SHAP)方法探索“三生空间”的特征重要性。结果显示:①本文提出的基于街景图像识别“三生空间”方法具有较好的效果,模型识别生产、生活和生态空间的R2均达到0.6,表明使用街景图像识别“三生空间”具有可行性;②分析研究区内的“三生空间”分布格局,研究区内以生产-生活空间为主,数量多且在区域内呈片状分布,以生态空间为主的单元数量少,主要分布在大型公园处;③分析7维场景层语义特征的重要性,其中街道开敞度和机动化程度对三类空间形成的影响最大。本研究成果丰富了“三生空间”识别的数据和方法体系,为城市空间结构优化和发展决策提供了新的工具。Understanding the urban spatial pattern from the perspective of the“Production-Living-Ecological”function not only paves the way to the optimization of land spatial structure,but also reflects the internal functional form and combination mode of urban land.However,in the past,the recognition of urban“Production-Living-Ecological Spaces”(PLES)mainly relied on remote sensing images,Point of Interest(POI),and land use data,and there was a lack of three-dimensional information within a city.Street View Images(SVI)can reflect the characteristics of the streets in the city and capture large-scale and high-resolution objective measurements of the physical environment within a street from a close-up view.Therefore,based on the semantic features of the scene extracted from the SVI,this paper proposes a method of identifying PLES in the central urban area and analyzing the importance of different features of the PLES.Taking the Fourth Ring Road of Chengdu as the study case,the classification system of PLES is constructed based on POI data,and the proportion of PLES is calculated at each SVI sampling point.The eXtreme Gradient Boosting(XGBoost)algorithm is used to identify the urban PLES,and a comparative test of model accuracy is also carried out.The spatial distribution of PLES in the study area is analyzed from three scales,i.e.,road network,500-m grid,and traffic analysis zone.The SHapley Additive exPlanation(SHAP)method is introduced to identify the important features that contribute to PLES.The results are as follows:(1)The proposed method of identifying PLES based on SVI in this paper has a high accuracy.The R2 of the model for identifying PLES reaches 0.6,indicating the feasibility of SVI for identifying PLES;(2)The spatial pattern of PLES reveals that the study area is dominated by production-living spaces,which are large in number and distributed in pieces in the study area.The number of units dominated by ecological space is small,and they are mainly distributed in large parks;(3)Among the semantic featur

关 键 词：三生空间 街景图像 兴趣点 空间格局 成都市 梯度极限提升算法 SHAP方法 交通分析区 

分 类 号：TU984.113[建筑科学—城市规划与设计] TP751[自动化与计算机技术—检测技术与自动化装置]