{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:23:25Z","timestamp":1773771805145,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T00:00:00Z","timestamp":1627603200000},"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 reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), and Support Vector Machines (SVMs) algorithms are used as the representatives for parametric, non-parametric and subpixel capable methods for change detection and change prediction of Urmia City (Iran) and its suburbs. Landsat images of 2000, 2010, and 2020 have been used to provide land cover information. The results demonstrated 0.93\u20130.94 overall accuracies for MLC and SVMs\u2019 algorithms, but it was around 0.79 for the SAM algorithm. The MLC performed slightly better than SVMs\u2019 classifier. Cellular Automata Artificial neural network method was used to predict land cover changes. Overall accuracy of MLC was higher than others at about 0.94 accuracy, although, SVMs were slightly more accurate for large area segments. Land cover maps were predicted for 2030, which demonstrate the city\u2019s expansion from 5500 ha in 2000 to more than 9000 ha in 2030.<\/jats:p>","DOI":"10.3390\/ijgi10080513","type":"journal-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T12:59:24Z","timestamp":1627649964000},"page":"513","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs\u2019 Classifiers Incorporating Artificial Neural Network Using Landsat Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Saeid","family":"Zare Naghadehi","sequence":"first","affiliation":[{"name":"Department of Environmental Resources Engineering, College of Environmental Science and Forestry, State University of New York, 1 Forestry Drive, Syracuse, NY 13210, USA"}]},{"given":"Milad","family":"Asadi","sequence":"additional","affiliation":[{"name":"Department of Urban Planning, University of Daneshpajoohan, Isfahan 81747-47144, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2114-7453","authenticated-orcid":false,"given":"Mohammad","family":"Maleki","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Kharazmi University, Tehran 37551-31979, Iran"}]},{"given":"Seyed-Mohammad","family":"Tavakkoli-Sabour","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Kharazmi University, Tehran 37551-31979, Iran"},{"name":"Tauber Delaburation, 99099 Erfurt, Germany"}]},{"given":"John Lodewijk","family":"Van Genderen","sequence":"additional","affiliation":[{"name":"International Institute for Geo-Information Science and Earth Observation (ITC), 7500 Enschede, The Netherlands"}]},{"given":"Samira-Sadat","family":"Saleh","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Kharazmi University, Tehran 37551-31979, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,30]]},"reference":[{"key":"ref_1","unstructured":"Centre for Health Development, World Health Organization (2010). 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