Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City
Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aim...
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MDPI AG
2025-07-01
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| author | Ziyu Liu Yacheng Song |
| author_facet | Ziyu Liu Yacheng Song |
| author_sort | Ziyu Liu |
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| description | Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management. |
| format | Article |
| id | doaj-art-015241a41e574843bd9b1d61dcb5907d |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-015241a41e574843bd9b1d61dcb5907d2025-08-20T03:35:38ZengMDPI AGLand2073-445X2025-07-01147146910.3390/land14071469Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled CityZiyu Liu0Yacheng Song1School of Architecture, Southeast University, Nanjing 210096, ChinaSchool of Architecture, Southeast University, Nanjing 210096, ChinaUrban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management.https://www.mdpi.com/2073-445X/14/7/1469urban morphologyplot classificationgraph attention networkspatial relationshipsurban renewal |
| spellingShingle | Ziyu Liu Yacheng Song Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City Land urban morphology plot classification graph attention network spatial relationships urban renewal |
| title | Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City |
| title_full | Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City |
| title_fullStr | Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City |
| title_full_unstemmed | Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City |
| title_short | Unsupervised Plot Morphology Classification via Graph Attention Networks: Evidence from Nanjing’s Walled City |
| title_sort | unsupervised plot morphology classification via graph attention networks evidence from nanjing s walled city |
| topic | urban morphology plot classification graph attention network spatial relationships urban renewal |
| url | https://www.mdpi.com/2073-445X/14/7/1469 |
| work_keys_str_mv | AT ziyuliu unsupervisedplotmorphologyclassificationviagraphattentionnetworksevidencefromnanjingswalledcity AT yachengsong unsupervisedplotmorphologyclassificationviagraphattentionnetworksevidencefromnanjingswalledcity |