{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T13:42:39Z","timestamp":1782999759764,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,2]],"date-time":"2023-07-02T00:00:00Z","timestamp":1688256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771473"],"award-info":[{"award-number":["41771473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["HCYK2022Y20"],"award-info":[{"award-number":["HCYK2022Y20"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Changjiang National Cultural Park Construction Research Project","award":["41771473"],"award-info":[{"award-number":["41771473"]}]},{"name":"Hubei Changjiang National Cultural Park Construction Research Project","award":["HCYK2022Y20"],"award-info":[{"award-number":["HCYK2022Y20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The visual quality and spatial distribution of architectural styles represent a city\u2019s image, influence inhabitants\u2019 living conditions, and may have positive or negative social consequences which are critical to urban sensing and designing. Conventional methods of identifying architectural styles rely on human labor and are frequently time-consuming, inefficient, and subjective in judgment. These issues significantly affect the large-scale management of urban architectural styles. Fortunately, deep learning models have robust feature expression abilities for images and have achieved highly competitive results in object detection in recent years. They provide a new approach to supporting traditional architectural style recognition. Therefore, this paper summarizes 22 architectural styles in a study area which could be used to define and describe urban architectural styles in most Chinese urban areas. Then, this paper introduced a Faster-RCNN general framework of architectural style classification with a VGG-16 backbone network, which is the first machine learning approach to identifying architectural styles in Chinese cities. Finally, this paper introduces an approach to constructing an urban architectural style dataset by mapping the identified architectural style through continuous street view imagery and vector map data from a top-down building contour map. The experimental results show that the architectural style dataset created had a precision of 57.8%, a recall rate of 80.91%, and an F1 score of 0.634. This dataset can, to a certain extent, reflect the geographical distribution characteristics of a wide variety of urban architectural styles. The proposed approach could support urban design to improve a city\u2019s image.<\/jats:p>","DOI":"10.3390\/ijgi12070264","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:42:46Z","timestamp":1688344966000},"page":"264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Urban Architectural Style Recognition and Dataset Construction Method under Deep Learning of Street View Images: A Case Study of Wuhan"],"prefix":"10.3390","volume":"12","author":[{"given":"Hong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"},{"name":"Hubei Provincial Engineering Research Center of Urban Regeneration, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haozun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7502-9869","authenticated-orcid":false,"given":"Lubin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xincan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep Learning for Computer Vision: A Brief Review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. 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