Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2018.08.020 |
Measuring human perceptions of a large-scale urban region using machine learning | |
Zhang, Fan1,2,3; Zhou, Bolei4; Liu, Liu5; Liu, Yu1; Fung, Helene H.6; Lin, Hui2,7; Ratti, Carlo3 | |
2018-12-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING |
ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2018 |
卷号 | 180页码:148-160 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | Measuring the human sense of place and quantifying the connections among the visual features of the built environment that impact the human sense of place have long been of interest to a wide variety of fields. Previous studies have relied on low-throughput surveys and limited data sources, which have difficulty in measuring the human perception of a large-scale urban region at flexible spatial resolutions. In this work, a data-driven machine learning approach is proposed to measure how people perceive a place in a large-scale urban region. Specifically, a deep learning model, which has been trained on millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image. The model achieved a high accuracy rate in predicting six human perceptual indicators, namely, safe, lively, beautiful, wealthy, depressing, and boring. This model can help to map the distribution of the city-wide human perception for a new urban region. Furthermore, a series of statistical analyses was conducted to determine the visual elements that may cause a place to be perceived as different perceptions. From the 150 object categories segmented from the street view images, various objects were identified as being positively or negatively correlated with each of the six perceptual indicators. The results take researchers and urban planners one step toward understanding the interactions of the place sentiments and semantics. |
英文关键词 | Urban perception Place semantics Street-level imagery Deep learning Built environment |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000449896300016 |
WOS关键词 | GOOGLE STREET VIEW ; CRIME ; WORLD |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/25347 |
专题 | 资源环境科学 |
作者单位 | 1.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China; 2.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Hong Kong, Peoples R China; 3.MIT, Senseable City Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA; 4.MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA; 5.China Acad Urban Planning & Design, Shanghai Branch, Shanghai 200335, Peoples R China; 6.Chinese Univ Hong Kong, Dept Psychol, Hong Kong, Hong Kong, Peoples R China; 7.Jiangxi Normal Univ, Coll Geog & Environm, Nanchang 330200, Jiangxi, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Fan,Zhou, Bolei,Liu, Liu,et al. Measuring human perceptions of a large-scale urban region using machine learning[J]. LANDSCAPE AND URBAN PLANNING,2018,180:148-160. |
APA | Zhang, Fan.,Zhou, Bolei.,Liu, Liu.,Liu, Yu.,Fung, Helene H..,...&Ratti, Carlo.(2018).Measuring human perceptions of a large-scale urban region using machine learning.LANDSCAPE AND URBAN PLANNING,180,148-160. |
MLA | Zhang, Fan,et al."Measuring human perceptions of a large-scale urban region using machine learning".LANDSCAPE AND URBAN PLANNING 180(2018):148-160. |
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