References
[1]. Li Y C, Peng L, Wu C W and Zhang J Z 2002 Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review. Buildings-Basel 12(8) 1167
[2]. Zhang F and Liu Y 2021 Street view imagery:Methods and applications based on artificial intelligence. Nrsb 25(5) 1043-1054
[3]. Zhou B L, Lapedriza A, Khosla A, Oliva A and Torralba A 2018 Places: A 10 Million Image Database for Scene Recognition. Tpami 40(6) 1452-1464
[4]. Seiferling I, Naik N, Ratti C and Proulx R 2017 Green streets - Quantifying and mapping urban trees with street-level imagery and computer vision. Landscape urban plan 165 93-101
[5]. Stubbing P, Peskett J, Rowe F and Arribas-Bel D 2019 A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning. Remote Sens-Basel 11(12) 1395
[6]. Gong F Y, Zeng Z C, Zhang F, Li X J, Ng E and Norford L K 2018 Mapping sky, tree, and building view factors of street canyons in a high-density urban environment. Build environ 134 155-167
[7]. Li X J and Ratti C 2019 Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landscape urban plan 191 103387
[8]. Li X J, Cai B Y, Qiu W S, Zhao J H and Ratti C 2019 A novel method for predicting and mapping the occurrence of sun glare using Google Street View. Transport res c-emer 106 132-144
[9]. Zhang F, Zhou B L, Liu L, Liu Y, Fung H H, Lin H and Ratti C 2018 Measuring human perceptions of a large-scale urban region using machine learning. Landscape urban plan 180 148-160
[10]. Amiruzzaman M, Curtis A, Zhao Y, Jamonnak S and Ye X Y 2021 Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach. Jcss 4(2) 813-837
[11]. Zhang F, Fan Z Y, Kang Y H, Hu Y J and Ratti C 2021 “Perception bias”: Deciphering a mismatch between urban crime and perception of safety. Landscape urban plan 207 104003
[12]. Nguyen Q C, Sajjadi M, McCullough M, Pham M, Nguyen T T, Yu W J, Meng H W, Wen M, Li F F, Smith K R, Brunisholz K and Tasdizen T 2018 Neighbourhood looking glass: 360 degrees automated characterisation of the built environment for neighbourhood effects research. Jech 72(3) 260-266
[13]. Nguyen Q C, Huang Y R, Kumar A, Duan H S, Keralis J M, Dwivedi P, Meng H W, Brunisholz K D, Jay J, Javanmardi M and Tasdizen T 2020 Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. Int J Env Res Pub He 17(17) 6359
[14]. Ma R X, Wang W, Zhang F, Shim K and Ratti C 2019 Typeface Reveals Spatial Economical Patterns. Sci Rep-uk 9 15946
[15]. Gebru T, Krause J, Wang Y L, Chen D Y, Deng J, Aiden E L and Li F F 2017 Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. P natl Acad Sci 114(50) 13108-13113
[16]. Joglekar S, Quercia D, Redi M, Aiello L M, Kauer T and Sastry N 2020 FaceLift: a transparent deep learning framework to beautify urban scenes. Roy Soc OpenSci 7(1) 190987
Cite this article
Song,H. (2024). Street view imagery: AI-based analysis method and application. Applied and Computational Engineering,40,54-62.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Li Y C, Peng L, Wu C W and Zhang J Z 2002 Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review. Buildings-Basel 12(8) 1167
[2]. Zhang F and Liu Y 2021 Street view imagery:Methods and applications based on artificial intelligence. Nrsb 25(5) 1043-1054
[3]. Zhou B L, Lapedriza A, Khosla A, Oliva A and Torralba A 2018 Places: A 10 Million Image Database for Scene Recognition. Tpami 40(6) 1452-1464
[4]. Seiferling I, Naik N, Ratti C and Proulx R 2017 Green streets - Quantifying and mapping urban trees with street-level imagery and computer vision. Landscape urban plan 165 93-101
[5]. Stubbing P, Peskett J, Rowe F and Arribas-Bel D 2019 A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning. Remote Sens-Basel 11(12) 1395
[6]. Gong F Y, Zeng Z C, Zhang F, Li X J, Ng E and Norford L K 2018 Mapping sky, tree, and building view factors of street canyons in a high-density urban environment. Build environ 134 155-167
[7]. Li X J and Ratti C 2019 Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landscape urban plan 191 103387
[8]. Li X J, Cai B Y, Qiu W S, Zhao J H and Ratti C 2019 A novel method for predicting and mapping the occurrence of sun glare using Google Street View. Transport res c-emer 106 132-144
[9]. Zhang F, Zhou B L, Liu L, Liu Y, Fung H H, Lin H and Ratti C 2018 Measuring human perceptions of a large-scale urban region using machine learning. Landscape urban plan 180 148-160
[10]. Amiruzzaman M, Curtis A, Zhao Y, Jamonnak S and Ye X Y 2021 Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach. Jcss 4(2) 813-837
[11]. Zhang F, Fan Z Y, Kang Y H, Hu Y J and Ratti C 2021 “Perception bias”: Deciphering a mismatch between urban crime and perception of safety. Landscape urban plan 207 104003
[12]. Nguyen Q C, Sajjadi M, McCullough M, Pham M, Nguyen T T, Yu W J, Meng H W, Wen M, Li F F, Smith K R, Brunisholz K and Tasdizen T 2018 Neighbourhood looking glass: 360 degrees automated characterisation of the built environment for neighbourhood effects research. Jech 72(3) 260-266
[13]. Nguyen Q C, Huang Y R, Kumar A, Duan H S, Keralis J M, Dwivedi P, Meng H W, Brunisholz K D, Jay J, Javanmardi M and Tasdizen T 2020 Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. Int J Env Res Pub He 17(17) 6359
[14]. Ma R X, Wang W, Zhang F, Shim K and Ratti C 2019 Typeface Reveals Spatial Economical Patterns. Sci Rep-uk 9 15946
[15]. Gebru T, Krause J, Wang Y L, Chen D Y, Deng J, Aiden E L and Li F F 2017 Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. P natl Acad Sci 114(50) 13108-13113
[16]. Joglekar S, Quercia D, Redi M, Aiello L M, Kauer T and Sastry N 2020 FaceLift: a transparent deep learning framework to beautify urban scenes. Roy Soc OpenSci 7(1) 190987