Multi-scale urban spatial pattern recognition based on skeleton line

doi:10.3785/j.issn.1008-973X.2026.06.017

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (6): 1299-1306 DOI: 10.3785/j.issn.1008-973X.2026.06.017

Multi-scale urban spatial pattern recognition based on skeleton line

Haoran SONG1,2,3(

),Xiaomin LU1,2,3,*(

),Zhiyi ZHANG1,2,3,Haowen YAN1,2,3,Chi ZOU1,2,3

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Key Laboratory of Science and Technology in Surveying and Mapping, Gansu Province, Lanzhou 730070, China

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Abstract

A multi-level urban spatial pattern recognition method based on road network skeleton line was proposed to reveal urban hierarchy and complexity in order to address the limitation where existing research on road network and building group was often confined to single-level analysis. A road network evaluation system was constructed by integrating space syntax and fractal geometry in order to extract macroscopic skeleton lines and analyze their topology. Road mesh was automatically extracted via morphological quantification and adjacency analysis. Spatial pattern of road mesh was identified by using the minimum spanning tree (MST) algorithm. Building was partitioned based on mesoscopic skeleton line. Automatic extraction and pattern recognition of building group were achieved by using hierarchical density-based spatial clustering of application with noise (HDBSCAN), Prim’s MST algorithm and the C4.5 decision tree. Validations in Shanghai, Chengdu, Wuhan and Lanzhou demonstrated the effectiveness of the method. The method can be used to efficiently and accurately identify urban spatial pattern across different levels.

Key words： multi-scale road network building group pattern recognition map generalization minimum spanning tree (MST)

Received: 10 July 2025 Published: 06 May 2026

CLC:

P 208

Fund: 国家自然科学基金资助项目(42161066, 42471476)；甘肃省自然科学基金资助项目(24JRRA224).

Corresponding Authors: Xiaomin LU E-mail: songhaoran0533@163.com;xiaominlu08@mail.lzjtu.cn

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Cite this article:

Haoran SONG,Xiaomin LU,Zhiyi ZHANG,Haowen YAN,Chi ZOU. Multi-scale urban spatial pattern recognition based on skeleton line. Journal of ZheJiang University (Engineering Science), 2026, 60(6): 1299-1306.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.06.017 OR https://www.zjujournals.com/eng/Y2026/V60/I6/1299

基于骨架线的多层级城市空间模式识别

针对现有研究在分析道路网与建筑物群组几何拓扑特征时多局限于单一层级识别的局限性，提出基于道路网骨架线的多层级城市空间分布模式识别方法，旨在揭示城市空间的层次性与复杂性. 整合空间句法和分形几何理论，构建道路网评价体系，提取宏观骨架线并分析拓扑结构. 通过形态量化和邻接关系分析，自动提取道路网眼，利用最小生成树(MST)算法识别网眼的空间模式. 基于中观骨架线划分建筑物，结合基于层次密度的噪声应用空间聚类(HDBSCAN)、Prim最小生成树算法及C4.5决策树，实现建筑物群组的自动提取与模式识别. 以上海、成都、武汉、兰州为实验区，验证了该方法的有效性. 结果表明，采用该方法，能够高效、准确地识别不同层级下的城市空间分布模式.

关键词： 多尺度道路网, 建筑物群组, 模式识别, 地图综合, 最小生成树(MST)

Fig.1 Multi-level recognition framework based on skeleton line

Fig.2 Principle of stroke construction

Fig.3 Frequency of parameter occurrence and corresponding research object

Fig.4 Result of skeleton line extracted according to different proportion

Fig.5 Hierarchical division based on skeleton line

Fig.6 Angle distribution of mesh

Fig.7 Building group identification process based on C4.5 algorithm and MST

Tab.1 Evaluation result of skeletonization at different proportion

Tab.2 Weight allocation of road network feature parameter based on entropy method

Fig.8 k-distance curve and inflection point of centroid of building group

Tab.3 Evaluation result of questionnaire survey on spatial pattern recognition effectiveness

Tab.4 Calculation result of road network indicator for each city in macroscopic identification process

Fig.9 Road network pattern recognition at macro level

Fig.10 Mesh pattern recognition at meso level

Fig.11 Boxplot generated from 300 building group

Tab.5 Classification accuracy, recall and F₁ score of proposed method and comparative experiment %

Fig.12 Building group pattern recognition at micro level


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