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Extracting multilane roads and establishing a high level of expressed detail play important roles in the field of automated cartographic generalization. An accurate and detailed extraction process benefits geographic analysis, urban region division, and road network construction, as well as transportation applications services. The road networks in OSM have a high level of detail and complex structures; however, they also include many duplicate lines, which degrade the efficiency and increase the difficulty of extracting multilane roads. To resolve these problems, this work proposes a machine\u2010learning\u2010based approach, in which the road networks are first converted from lines to polygons. Then, various geometric descriptors, including compactness, width, circularity, area, perimeter, complexity, parallelism, shape descriptor, and width\u2010to\u2010length ratio, are used to train a random forest (RF) classifier and identify the candidates. Finally, another RF is trained to evaluate the candidates using all the geometric descriptors and topological features; the outputs of this second trained RF are the predicted multilane roads. An experiment using OSM data from Beijing, China validated the proposed method, which achieves a highly effective performance when extracting multilane roads from OSM.<\/jats:p>","DOI":"10.1111\/tgis.12514","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T02:29:18Z","timestamp":1545877758000},"page":"224-240","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Multilane roads extracted from the OpenStreetMap urban road network using random forests"],"prefix":"10.1111","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering China University of Geosciences Wuhan China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering China University of Geosciences Wuhan China"},{"name":"National Engineering Research Center of Geographic Information System Wuhan China"}]},{"given":"Liang","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering China University of Geosciences Wuhan China"},{"name":"National Engineering Research Center of Geographic Information System Wuhan China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6373-3162","authenticated-orcid":false,"given":"Zhanlong","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering China University of Geosciences Wuhan China"}]}],"member":"311","published-online":{"date-parts":[[2018,12,26]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-007-0026-z"},{"key":"e_1_2_6_3_1","volume-title":"Proceedings of the 22nd International Cartographic Association Conference","author":"Chaudhry O.","year":"2005"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.3138\/FM57-6770-U75U-7727"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/10724117.2002.11974602"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.08.011"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10708-007-9111-y"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2012.03.002"},{"issue":"7","key":"e_1_2_6_9_1","first-page":"761","article-title":"Intelligent road\u2010network selection using cases based reasoning","volume":"43","author":"Guo M.","year":"2014","journal-title":"Acta Geodaetica Et Cartographica Sinica"},{"key":"e_1_2_6_10_1","volume-title":"Knowledge discovery with support vector machines","author":"Hamel L. 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