Urban street network morphology classification through street-block based graph neural networks and multi-model fusion
Precise categorization of urban street network patterns is essential for urban planning and morphology analysis. Current classification methods typically rely on a single model type, which causes them to struggle in considering topological, geometric, visual, and global features simultaneously, lead...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2497490 |
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| _version_ | 1849224286665965568 |
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| author | Yang Liu Qingsheng Guo Chuanbang Zheng |
| author_facet | Yang Liu Qingsheng Guo Chuanbang Zheng |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Precise categorization of urban street network patterns is essential for urban planning and morphology analysis. Current classification methods typically rely on a single model type, which causes them to struggle in considering topological, geometric, visual, and global features simultaneously, leading to suboptimal results. To address this, we propose a novel fusion model that integrates three submodels: our proposed street-block graph neural network (SBGNet), a convolutional neural network (CNN) using ResNet-34, and a multi-layer perceptron (MLP). SBGNet represents urban street networks as graphs, with nodes as street blocks and geometric features as node attributes, while spatial arrangements serve as edge features. Topological and geometric features are extracted from this street-block representation. The CNN extracts visual features from monochrome images of the street network, and the MLP computes global descriptors using OSMnx and NetworkX to extract global features. Features from SBGNet, CNN, and MLP are concatenated and processed through a downstream MLP for classification. Experiments on a dataset of 6,700 samples from 12 major U.S. cities demonstrate that our fusion model significantly outperforms traditional methods, achieving a PR AUC of 0.88 ± 0.01, with improvements of 5% to 33% over baseline models, validating its effectiveness in classifying complex urban street networks. |
| format | Article |
| id | doaj-art-4e4fd027316841bba21cbb3cdb7f4cf7 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-4e4fd027316841bba21cbb3cdb7f4cf72025-08-25T11:28:39ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2497490Urban street network morphology classification through street-block based graph neural networks and multi-model fusionYang Liu0Qingsheng Guo1Chuanbang Zheng2School of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaPrecise categorization of urban street network patterns is essential for urban planning and morphology analysis. Current classification methods typically rely on a single model type, which causes them to struggle in considering topological, geometric, visual, and global features simultaneously, leading to suboptimal results. To address this, we propose a novel fusion model that integrates three submodels: our proposed street-block graph neural network (SBGNet), a convolutional neural network (CNN) using ResNet-34, and a multi-layer perceptron (MLP). SBGNet represents urban street networks as graphs, with nodes as street blocks and geometric features as node attributes, while spatial arrangements serve as edge features. Topological and geometric features are extracted from this street-block representation. The CNN extracts visual features from monochrome images of the street network, and the MLP computes global descriptors using OSMnx and NetworkX to extract global features. Features from SBGNet, CNN, and MLP are concatenated and processed through a downstream MLP for classification. Experiments on a dataset of 6,700 samples from 12 major U.S. cities demonstrate that our fusion model significantly outperforms traditional methods, achieving a PR AUC of 0.88 ± 0.01, with improvements of 5% to 33% over baseline models, validating its effectiveness in classifying complex urban street networks.https://www.tandfonline.com/doi/10.1080/17538947.2025.2497490Urban morphologygraph neural networks (GNN)street-block graph representationmulti-model fusionurban street network classification |
| spellingShingle | Yang Liu Qingsheng Guo Chuanbang Zheng Urban street network morphology classification through street-block based graph neural networks and multi-model fusion International Journal of Digital Earth Urban morphology graph neural networks (GNN) street-block graph representation multi-model fusion urban street network classification |
| title | Urban street network morphology classification through street-block based graph neural networks and multi-model fusion |
| title_full | Urban street network morphology classification through street-block based graph neural networks and multi-model fusion |
| title_fullStr | Urban street network morphology classification through street-block based graph neural networks and multi-model fusion |
| title_full_unstemmed | Urban street network morphology classification through street-block based graph neural networks and multi-model fusion |
| title_short | Urban street network morphology classification through street-block based graph neural networks and multi-model fusion |
| title_sort | urban street network morphology classification through street block based graph neural networks and multi model fusion |
| topic | Urban morphology graph neural networks (GNN) street-block graph representation multi-model fusion urban street network classification |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2497490 |
| work_keys_str_mv | AT yangliu urbanstreetnetworkmorphologyclassificationthroughstreetblockbasedgraphneuralnetworksandmultimodelfusion AT qingshengguo urbanstreetnetworkmorphologyclassificationthroughstreetblockbasedgraphneuralnetworksandmultimodelfusion AT chuanbangzheng urbanstreetnetworkmorphologyclassificationthroughstreetblockbasedgraphneuralnetworksandmultimodelfusion |