{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:20:52Z","timestamp":1760059252431,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of the Jiangxi Provincial Department of Education","award":["GJJ200454"],"award-info":[{"award-number":["GJJ200454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate clustering of buildings is a prerequisite for map generalization in densely populated urban data. Edge buildings at the edge of building groups, identified through human-eye recognition, may serve as boundary constraints for clustering. This paper proposes the use of seven Gestalt factors to distinguish edge buildings from other buildings. Employing the DGI model to produce high-quality node embeddings, optimize the mutual information between the local node representation and the global summary vector. We then conduct training to identify edge buildings in the two test datasets using eight feature combinations. This research introduces a modified distance metric called the \u2018m_dis\u2019 feature, which is used to describe the closeness between two adjacent buildings. Finally, the clusters of edge and inner buildings are determined through a constrained graph traversal that is based on the \u2018m_dis\u2019 feature. This method is capable of effectively identifying and distinguishing densely distributed building groups in Chengdu City, China, as demonstrated by experimental results. It offers novel concepts for edge building recognition in dense urban areas, confirms the significance of the LOF factor and the \u2018m_dis\u2019 feature, and achieves superior clustering results in comparison to other methods. Additionally, this semi-supervised clustering method (DGI-EIC) has the potential to achieve an ARI index of approximately 0.5.<\/jats:p>","DOI":"10.3390\/ijgi14060222","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T11:55:52Z","timestamp":1748951752000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Method for Edge and Inner Buildings Based on DGI Model and Graph Traversal"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5384-2023","authenticated-orcid":false,"given":"Hesheng","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, School of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chengdu Planning Research and Application Technology Center, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","first-page":"1534","article-title":"Towards a Scale-driven Theory for Spatial Clustering","volume":"46","author":"Li","year":"2017","journal-title":"Acta Geod. 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