Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes
Recognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recogniti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/213 |
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| author | Taiyang Yang Pengxin Zhang Daozhu Xu Pengcheng Liu Min Yang |
| author_facet | Taiyang Yang Pengxin Zhang Daozhu Xu Pengcheng Liu Min Yang |
| author_sort | Taiyang Yang |
| collection | DOAJ |
| description | Recognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recognition. To overcome these challenges, this study introduces a flow-based community search approach, which models morphological, functional, and spatial relationships with a graph-based representation. The approach consists of graph representation learning, flow-based community generation, and community quality assessment, enabling adaptive building group recognition based on both structural coherence and functional similarity. Experimental results on commercial complex recognition demonstrate that our approach consistently outperforms traditional methods, achieving an improvement of over 5.4% in F1 score compared to the second-best method. Furthermore, its strong performance on limited training datasets highlights its robustness. These findings establish the proposed approach as an effective and reliable tool for recognizing functionally cohesive building groups, with practical viability in urban planning and policy formulation. |
| format | Article |
| id | doaj-art-a52829944be74e6391ee66d94bcbe6d3 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-a52829944be74e6391ee66d94bcbe6d32025-08-20T02:21:12ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-05-0114621310.3390/ijgi14060213Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial ComplexesTaiyang Yang0Pengxin Zhang1Daozhu Xu2Pengcheng Liu3Min Yang4Key Laboratory of Smart Earth, Beijing 100029, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaKey Laboratory of Smart Earth, Beijing 100029, ChinaCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, ChinaKey Laboratory of Smart Earth, Beijing 100029, ChinaRecognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recognition. To overcome these challenges, this study introduces a flow-based community search approach, which models morphological, functional, and spatial relationships with a graph-based representation. The approach consists of graph representation learning, flow-based community generation, and community quality assessment, enabling adaptive building group recognition based on both structural coherence and functional similarity. Experimental results on commercial complex recognition demonstrate that our approach consistently outperforms traditional methods, achieving an improvement of over 5.4% in F1 score compared to the second-best method. Furthermore, its strong performance on limited training datasets highlights its robustness. These findings establish the proposed approach as an effective and reliable tool for recognizing functionally cohesive building groups, with practical viability in urban planning and policy formulation.https://www.mdpi.com/2220-9964/14/6/213building group recognitioncommunity searchcommercial complexesflow-based generation |
| spellingShingle | Taiyang Yang Pengxin Zhang Daozhu Xu Pengcheng Liu Min Yang Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes ISPRS International Journal of Geo-Information building group recognition community search commercial complexes flow-based generation |
| title | Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes |
| title_full | Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes |
| title_fullStr | Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes |
| title_full_unstemmed | Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes |
| title_short | Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes |
| title_sort | flow based community search approach for functionally cohesive building group recognition a case study on commercial complexes |
| topic | building group recognition community search commercial complexes flow-based generation |
| url | https://www.mdpi.com/2220-9964/14/6/213 |
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