{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:53:38Z","timestamp":1772542418175,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Science Foundation of the Ministry of Education of China","award":["18YJA760015"],"award-info":[{"award-number":["18YJA760015"]}]},{"name":"Humanities and Social Science Foundation of the Ministry of Education of China","award":["41971331"],"award-info":[{"award-number":["41971331"]}]},{"name":"National Natural Science Foundation of China","award":["18YJA760015"],"award-info":[{"award-number":["18YJA760015"]}]},{"name":"National Natural Science Foundation of China","award":["41971331"],"award-info":[{"award-number":["41971331"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The physical presence of a street, called the \u201cstreet view\u201d, is a medium through which people perceive the urban form. A street\u2019s spatial ratio is the main feature of the street view, and its measurement and quality are the core issues in the field of urban design. The traditional method of studying urban aspect ratios is manual on-site observation, which is inefficient, incomplete and inaccurate, making it difficult to reveal overall patterns and influencing factors. Street view images (SVI) provide large-scale urban data that, combined with deep learning algorithms, allow for studying street spatial ratios from a broader space-time perspective. This approach can reveal an urban forms\u2019 aesthetics, spatial quality, and evolution process. However, current streetscape research mainly focuses on the creation and maintenance of spatial data infrastructure, street greening, street safety, urban vitality, etc. In this study, quantitative research of the Beijing street spatial ratio was carried out using street view images, a convolution neural network algorithm, and the classical street spatial ratio theory of urban morphology. Using the DenseNet model, the quantitative measurement of Beijing\u2019s urban street location, street aspect ratio, and the street symmetry was realized. According to the model identification results, the law of the gradual transition of the street spatial ratio was depicted (from the open and balanced type to the canyon type and from the historical to the modern). Changes in the streets\u2019 spatiotemporal characteristics in the central area of Beijing were revealed. Based on this, the clustering and distribution phenomena of four street aspect ratio types in Beijing are discussed and the relationship between the street aspect ratio type and symmetry is summarized, selecting a typical lot for empirical research. The classical theory of street spatial proportion has limitations under the conditions of high-density development in modern cities, and the traditional urban morphology theory, combined with new technical methods such as streetscape images and deep learning algorithms, can provide new ideas for the study of urban space morphology.<\/jats:p>","DOI":"10.3390\/ijgi12060246","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Quantifying the Spatial Ratio of Streets in Beijing Based on Street-View Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Wei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Jiachen","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1562-6228","authenticated-orcid":false,"given":"Yong","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"given":"Mei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Design and Art, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Menghan","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101706","DOI":"10.1016\/j.compenvurbsys.2021.101706","article-title":"Classification of urban morphology with deep learning: Application on urban vitality","volume":"90","author":"Chen","year":"2021","journal-title":"Comput. 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