{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:38:17Z","timestamp":1781879897456,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"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>Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert\u2019s opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation.<\/jats:p>","DOI":"10.3390\/ijgi13030080","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Smart Urban Cadastral Map Enrichment\u2014A Machine Learning Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6138-8657","authenticated-orcid":false,"given":"Alireza","family":"Hajiheidari","sequence":"first","affiliation":[{"name":"GIS Department, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417614411, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9654-6491","authenticated-orcid":false,"given":"Mahmoud Reza","family":"Delavar","sequence":"additional","affiliation":[{"name":"Center of Excellence in Geomatic Engineering in Disaster Management, Land Administration in Smart City Laboratory, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417614411, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4256-3173","authenticated-orcid":false,"given":"Abbas","family":"Rajabifard","sequence":"additional","affiliation":[{"name":"The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101895","DOI":"10.1016\/j.compenvurbsys.2022.101895","article-title":"Combining a Land Parcel Cellular Automata (LP-CA) Model with Participatory Approaches in the Simulation of Disruptive Future Scenarios of Urban Land Use Change","volume":"99","author":"Shurupov","year":"2023","journal-title":"Comput. 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