Mapping human perception of urban landscape from street-view images: A deep-learning approach
Human perception of urban landscape, which signifies to what extent urban landscape is appreciated by local dwellers, informs human-oriented policies that reinforce public participation. Yet, conventional studies on human perception of urban landscape are largely dependent on individual experience,...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222000887 |
| _version_ | 1828758485144174592 |
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| author | Jingxian Wei Wenze Yue Mengmeng Li Jiabin Gao |
| author_facet | Jingxian Wei Wenze Yue Mengmeng Li Jiabin Gao |
| author_sort | Jingxian Wei |
| collection | DOAJ |
| description | Human perception of urban landscape, which signifies to what extent urban landscape is appreciated by local dwellers, informs human-oriented policies that reinforce public participation. Yet, conventional studies on human perception of urban landscape are largely dependent on individual experience, which may restrict the co-production of knowledge that can be operationalized across spatial scales and sectors. In this study, we mapped human perception of urban landscape in Shanghai by leveraging an advanced deep-learning approach and street-view images. Specifically, the ResNet50 model was employed to map four critical perceptions, i.e., security, depression, vitality, and aesthetic, at parcel level. We further explored the relationship between human perception and land-use types. Our results show that highly urbanized area (Puxi district encompassed by the Inner Ring Road) is perceived as more secure and vital, but more depressing. Besides, human perception varies substantially across different land-use types, among which administrative and service land is favored with regard to all the four perception types. This study advances our understanding of urban landscape through the lens of human perception, and provides nuanced insights into steering human settlement towards sustainability by strategically promoting mixed land use. |
| first_indexed | 2024-12-11T00:42:42Z |
| format | Article |
| id | doaj.art-077659a4ec6f4c3aacec7f96b3a5ce45 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-11T00:42:42Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-077659a4ec6f4c3aacec7f96b3a5ce452022-12-22T01:26:52ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102886Mapping human perception of urban landscape from street-view images: A deep-learning approachJingxian Wei0Wenze Yue1Mengmeng Li2Jiabin Gao3Department of Land Management, Zhejiang University, Hangzhou 310029, P.R. ChinaDepartment of Land Management, Zhejiang University, Hangzhou 310029, P.R. China; Corresponding author.Department of Land Management, Zhejiang University, Hangzhou 310029, P.R. China; Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The NetherlandsBureau of Development, Reform and Economic Informatization, Shangcheng District, Hangzhou 310029, P.R. ChinaHuman perception of urban landscape, which signifies to what extent urban landscape is appreciated by local dwellers, informs human-oriented policies that reinforce public participation. Yet, conventional studies on human perception of urban landscape are largely dependent on individual experience, which may restrict the co-production of knowledge that can be operationalized across spatial scales and sectors. In this study, we mapped human perception of urban landscape in Shanghai by leveraging an advanced deep-learning approach and street-view images. Specifically, the ResNet50 model was employed to map four critical perceptions, i.e., security, depression, vitality, and aesthetic, at parcel level. We further explored the relationship between human perception and land-use types. Our results show that highly urbanized area (Puxi district encompassed by the Inner Ring Road) is perceived as more secure and vital, but more depressing. Besides, human perception varies substantially across different land-use types, among which administrative and service land is favored with regard to all the four perception types. This study advances our understanding of urban landscape through the lens of human perception, and provides nuanced insights into steering human settlement towards sustainability by strategically promoting mixed land use.http://www.sciencedirect.com/science/article/pii/S1569843222000887Human perceptionDeep learningStreet-view imageUrban landscapeLand use |
| spellingShingle | Jingxian Wei Wenze Yue Mengmeng Li Jiabin Gao Mapping human perception of urban landscape from street-view images: A deep-learning approach International Journal of Applied Earth Observations and Geoinformation Human perception Deep learning Street-view image Urban landscape Land use |
| title | Mapping human perception of urban landscape from street-view images: A deep-learning approach |
| title_full | Mapping human perception of urban landscape from street-view images: A deep-learning approach |
| title_fullStr | Mapping human perception of urban landscape from street-view images: A deep-learning approach |
| title_full_unstemmed | Mapping human perception of urban landscape from street-view images: A deep-learning approach |
| title_short | Mapping human perception of urban landscape from street-view images: A deep-learning approach |
| title_sort | mapping human perception of urban landscape from street view images a deep learning approach |
| topic | Human perception Deep learning Street-view image Urban landscape Land use |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222000887 |
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