{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:09:46Z","timestamp":1771470586056,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,18]],"date-time":"2017-10-18T00:00:00Z","timestamp":1508284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as \u201c0-eliminated,\u201d \u201c1-retained,\u201d or \u201c2-aggregated.\u201d Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.<\/jats:p>","DOI":"10.3390\/ijgi6100309","type":"journal-article","created":{"date-parts":[[2017,10,18]],"date-time":"2017-10-18T11:10:00Z","timestamp":1508325000000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Machine Learning Classification of Buildings for Map Generalization"],"prefix":"10.3390","volume":"6","author":[{"given":"Jaeeun","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanme","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghyeon","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiyun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"},{"name":"Institute of Construction and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,18]]},"reference":[{"key":"ref_1","unstructured":"Regnauld, N. (1996, January 12\u201316). Recognition of building clusters for generalization. Proceedings of the 7th International Symposium on Spatial Data Handling, Delft, The Netherlands."},{"key":"ref_2","first-page":"75","article-title":"Parameter-free cluster detection in spatial databases and its application to typification","volume":"33","author":"Anders","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1023\/A:1009713606524","article-title":"Conflict reduction in map generalization using iterative improvement","volume":"2","author":"Ware","year":"1998","journal-title":"GeoInformatica"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/13658810410001702021","article-title":"Automated building generalization based on urban morphology and Gestalt theory","volume":"18","author":"Li","year":"2004","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","first-page":"1","article-title":"Automatic generalization of roads and buildings","volume":"50","author":"Wang","year":"2004","journal-title":"Triangle"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Damen, J., Kreveld, M., and Spaan, B. (2007, January 2\u20133). High quality building generalization by extending the morphological operators. Proceedings of the 11th ICA Workshop on Generalisation and Multiple Representation, Moscow, Russia.","DOI":"10.1016\/B978-008045374-3\/50004-1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.7848\/ksgpc.2011.29.1.21","article-title":"A Study on the Consecutive Renewal of Road and Building Information in the Multi-Scale Digital Maps","volume":"29","author":"Park","year":"2011","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_8","unstructured":"Vetter, A., Wigley, M., K\u00e4uferle, D., and Gartner, G. (2015, January 21). The automatic generalisation of building polygons with arcgis standard tools based on the 1: 50,000 swiss national map series. Proceedings of the 18th ICA Workshop on Generalisation and Multiple Representation, Rio de Janeiro, Brazil."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1147\/rd.441.0206","article-title":"Some studies in machine learning using the game of checkers","volume":"44","author":"Samuel","year":"2000","journal-title":"IBM J. Res. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10707-007-0026-z","article-title":"Generalization-oriented road line classification by means of an artificial neural network","volume":"12","author":"Balboa","year":"2008","journal-title":"Geoinformatica"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hashemi, M., and Karimi, H.A. (2016, January 28\u201330). A Machine Learning Approach to Improve the Accuracy of GPS-Based Map-Matching Algorithms. Proceedings of the Information Reuse and Integration (IRI) 2016 IEEE 17th International Conference, Pittsburgh, PA, USA.","DOI":"10.1109\/IRI.2016.18"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1179\/1743277413Y.0000000042","article-title":"Use of artificial neural networks for selective omission in updating road networks","volume":"51","author":"Zhou","year":"2014","journal-title":"Cartogr. J."},{"key":"ref_13","unstructured":"Karsznia, I., and Weibel, R. (2017). Improving settlement selection for small-scale maps using data enrichment and machine learning. Cartogr. Geogr. Inf. Sci., 1\u201317."},{"key":"ref_14","unstructured":"(2017, June 26). National Geographic Information Institute. Available online: https:\/\/www.ngii.go.kr\/."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.7848\/ksgpc.2014.32.4-1.311","article-title":"Updating building data in digital topographic map based on matching and generation of update history record","volume":"32","author":"Park","year":"2014","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.7848\/ksgpc.2013.31.1.23","article-title":"Calculation of a Threshold for Decision of Similar Features in Different Spatial Data Sets","volume":"31","author":"Kim","year":"2013","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TPAMI.1984.4767546","article-title":"Analysis and design of a decision tree based on entropy reduction and its application to large character set recognition","volume":"PAMI-6","author":"Wang","year":"1984","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Murty, M.N., and Devi, V.S. (2011). Pattern Recognition: An Algorithmic Approach, Springer. [1st ed.].","DOI":"10.1007\/978-0-85729-495-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:47:45Z","timestamp":1760208465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,18]]},"references-count":19,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["ijgi6100309"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6100309","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,18]]}}}