{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:38:33Z","timestamp":1784821113621,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Sciences Foundation of China","award":["41561085"],"award-info":[{"award-number":["41561085"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>To address the challenges in current research on spatial clustering algorithms for buildings in topographic maps\u2014namely, their limited ability to effectively accommodate diverse application scenarios, including dense and regular urban environments, sparsely and irregularly distributed rural areas, and urban villages with complex structures\u2014this paper introduces an innovative progressive clustering algorithm framework. The proposed framework operates in a hierarchical manner, progressing from macro to micro levels, thereby enhancing its adaptability and practical versatility. Specifically, it employs the minimum spanning tree (MST) technique for macro-level clustering analysis. Subsequently, a self-organizing map (SOM) neural network is utilized to perform micro-level clustering, enabling a more refined and detailed classification. Within this framework, the minimum spanning tree effectively captures the macroscopic distribution patterns of the building population. The macroscopic clustering results are then utilized as the initial weight configurations for the SOM neural network. This approach ensures that the overall spatial structural integrity is preserved during the subsequent micro-level clustering process. Moreover, the SOM neural network achieves refined optimization of micro-clustering details by incorporating building feature factors. To validate the effectiveness of the proposed algorithm, this study conducts an empirical analysis and comparative testing using building data from Futian District, Shenzhen City. The results indicate that the proposed algorithm exhibits superior recognition capabilities when applied to complex and variable spatial distribution patterns of buildings. Furthermore, the clustering outcomes align closely with the principles of Gestalt visual perception and outperform the comparison algorithms in overall performance.<\/jats:p>","DOI":"10.3390\/ijgi14030103","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T13:39:06Z","timestamp":1740490746000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Progressive Clustering Approach for Buildings Using MST and SOM with Feature Factors"],"prefix":"10.3390","volume":"14","author":[{"given":"Tianliang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoji","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/02693798808927876","article-title":"A GIS research agenda","volume":"2","author":"Rhind","year":"1988","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.landurbplan.2017.03.003","article-title":"Measuring urban forms from inter-building distances: Combining MST graphs with a Local Index of Spatial Association","volume":"163","author":"Caruso","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_4","unstructured":"Zhao, Z. (2021). Research on Urban Spatial Pattern Supported by Building Footprint Data. [Ph.D. Thesis, Wuhan University]."},{"key":"ref_5","first-page":"382","article-title":"Cartographic-generalization-knowledge and its application","volume":"31","author":"Wang","year":"2006","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X., and Burghardt, D. (2019). A mesh-based typification method for building groups with grid patterns. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040168"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ai, T., and Zhang, X. (2007). The aggregation of urban building clusters based on the skeleton partitioning of gap space. The European Information Society: Leading the Way With Geo-Information, Springer.","DOI":"10.1007\/978-3-540-72385-1_9"},{"key":"ref_8","first-page":"302","article-title":"Polygon Cluster Pattern Mining Based on Gestalt Principles","volume":"36","author":"Ai","year":"2007","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1037\/h0072422","article-title":"Perception: An introduction to the Gestalt-Theorie","volume":"19","author":"Koffka","year":"1922","journal-title":"Psychol. Bull."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/10106049.2014.925002","article-title":"Proximity-based grouping of buildings in urban blocks: A comparison of four algorithms","volume":"30","author":"Cetinkaya","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1080\/13658816.2010.533674","article-title":"Detecting arbitrarily shaped clusters using ant colony optimization","volume":"25","author":"Pei","year":"2011","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/10106049.2018.1508313","article-title":"A local adaptive density-based algorithm for clustering polygonal buildings in urban block polygons","volume":"35","author":"Pilehforooshha","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3490384","article-title":"Experimental comparisons of clustering approaches for data representation","volume":"55","author":"Anand","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_14","first-page":"62","article-title":"The Research about Clustering Algorithm of K-Means","volume":"21","author":"Zhou","year":"2011","journal-title":"Comput. Technol. Dev."},{"key":"ref_15","unstructured":"Hu, X., Ma, R., and Zhong, B. (2010). Study on validity of hierarchical clustering. J. Shandong Univ., 40."},{"key":"ref_16","first-page":"35","article-title":"Support for Area-Based Target Constraints in Delaunay Triangulation for Map Generalization","volume":"1","author":"Ai","year":"2000","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_17","first-page":"1628","article-title":"Segmentation Region Density Clustering Algorithm Based on Minimum Spanning Tree","volume":"31","author":"Li","year":"2019","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"ref_18","unstructured":"Yan, Y., Zhang, X., and Wang, L. (2021). Decentralized Iterative Community Clustering Parallelization Based on Network Weighted Voronoi Diagram on Spark Platform. Comput. Appl. Softw., 38."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/10106049.2019.1590463","article-title":"A heuristic approach to the generalization of complex building groups in urban villages","volume":"36","author":"Yu","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_20","first-page":"631","article-title":"A Clustering Method of Rural Settlement Considering Direction Relation","volume":"48","author":"Lv","year":"2023","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_21","unstructured":"Gao, C. (2024). Research on Clustering Algorithm for Area Vector Building. [Master\u2019s Thesis, Chang\u2019an University]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1007\/s00453-001-0008-8","article-title":"Contextual building typification in automated map generalization","volume":"30","author":"Regnauld","year":"2001","journal-title":"Algorithmica"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.ins.2022.07.101","article-title":"A fast spectral clustering technique using MST based proximity graph for diversified datasets","volume":"609","author":"Khan","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"295","DOI":"10.3901\/JME.2023.07.295","article-title":"SOM-Kmeans Based Machine Tools Energy Efficiency Grade Evaluation","volume":"59","author":"Cui","year":"2023","journal-title":"J. Mech. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ai, T., Yin, H., Shen, Y., Yang, M., and Wang, L. (2019). A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0218877"},{"key":"ref_26","first-page":"316","article-title":"Research on hierarchical segmentation method of high-resolution remote sensing image based on minimum spanning tree model","volume":"51","author":"Lin","year":"2022","journal-title":"J. Geod. Geoinf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1090\/S0002-9939-1956-0078686-7","article-title":"On the shortest spanning subtree of a graph and the traveling salesman problem","volume":"7","author":"Kruskal","year":"1956","journal-title":"Proc. Am. Math. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1002\/j.1538-7305.1957.tb01515.x","article-title":"Shortest connection networks and some generalizations","volume":"36","author":"Prim","year":"1957","journal-title":"Bell Syst. Tech. J."},{"key":"ref_29","first-page":"247","article-title":"Building Clustering Based on Minimum Spanning Tree Algorithm","volume":"40","author":"Cai","year":"2017","journal-title":"Surv. Mapp."},{"key":"ref_30","first-page":"290","article-title":"Intelligent Building Grouping Using a Self-organizing Map","volume":"42","author":"Cheng","year":"2013","journal-title":"J. Geod. Geoinf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Z. (2016). The Cluster of Buildings Based on the SOM Neural Network. [Master\u2019s Thesis, University of Electronic Science and Technology].","DOI":"10.1088\/1755-1315\/57\/1\/012047"},{"key":"ref_32","first-page":"1530","article-title":"The Research on Initialization of Ants System and Configuration of Parameters for Different TSP Problems in Ant Algorithm","volume":"34","author":"Wu","year":"2006","journal-title":"Acta Electron. Sin."},{"key":"ref_33","first-page":"335","article-title":"Clustering Analysis of Geographical Area Entities Considering Distance and Shape Similarity","volume":"34","author":"Yang","year":"2009","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_34","unstructured":"Ruas, A. (2000, January 10\u201312). The roles of meso objects for generalisation. Proceedings of the 9th Symposium on Spatial Data Handling, Beijing, China."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/j.1467-9671.2004.00168.x","article-title":"Quality assessment of cartographic generalisation","volume":"8","author":"Bard","year":"2004","journal-title":"Trans. GIS"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1080\/13658816.2017.1346257","article-title":"Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS","volume":"31","author":"Basaraner","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","first-page":"1202","article-title":"Reasoning of spatial distribution pattern of building cluster based on geographic knowledge graph","volume":"25","author":"Tang","year":"2023","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_38","unstructured":"Berg, W.J. (1983). Semiology of Graphics: Diagrams, Networks, Maps, The University of Wisconstin Press."},{"key":"ref_39","first-page":"116","article-title":"Comparative Analysis of Clustering Methods of Planar Settlements","volume":"48","author":"Meng","year":"2023","journal-title":"J. Geomat."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.measurement.2019.01.013","article-title":"Application of improved som network in gene data cluster analysis","volume":"145","author":"Nan","year":"2019","journal-title":"Measurement"},{"key":"ref_41","first-page":"37","article-title":"A Study of Principal-Agent Model Based Spatial Form Differentiation in Urban Villages in Shenzhen, China","volume":"9","author":"Liu","year":"2023","journal-title":"Mod. Urban Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, R., Cheng, Q., and Chen, Z. (2021, January 10\u201311). An Algorithm of Data Classification Based on PCA and K-Means++. Proceedings of the 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Shenyang, China.","DOI":"10.1109\/TOCS53301.2021.9688593"},{"key":"ref_43","first-page":"204","article-title":"Comparative study on clustering adaptability of DBSCAN extended algorithm in planar buildings","volume":"47","author":"Meng","year":"2022","journal-title":"Sci. Surv. Mapp."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dinh, D.-T., Fujinami, T., and Huynh, V.-N. (December, January 29). Estimating the optimal number of clusters in categorical data clustering by silhouette coefficient. Proceedings of the Knowledge and Systems Sciences: 20th International Symposium, KSS 2019, Da Nang, Vietnam. Proceedings 20.","DOI":"10.1007\/978-981-15-1209-4_1"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, X., Huang, Q., and Gao, S. (2021). Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN. Uncertainty and Context in GIScience and Geography, Routledge.","DOI":"10.4324\/9781003123842-5"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/3\/103\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:42:33Z","timestamp":1760028153000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/3\/103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["ijgi14030103"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14030103","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]}}}