Urban street network morphology classification through street-block based graph neural networks and multi-model fusion

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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Bibliographic Details
Main Authors: Yang Liu, Qingsheng Guo, Chuanbang Zheng
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
Subjects:
Urban morphology
graph neural networks (GNN)
street-block graph representation
multi-model fusion
urban street network classification
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2497490
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https://www.tandfonline.com/doi/10.1080/17538947.2025.2497490

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