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Received：27 September 2019，
Published：07 May 2021
移动端阅览
街景影像是感知城市物质环境的一种新型地理大数据。这种数据以人的视角详尽描绘了城市的可视环境，同时也隐性地表达了可视环境背后有关城市功能、社会经济和人类活动的信息。然而，传统的数字图像处理技术对街景影像的处理能力有限，不能高效地理解其中的语义信息。近年来，随着人工智能领域的不断发展，图像分析和机器学习方法取得了突破性的进展。以深度学习和计算机视觉为代表的前沿人工智能技术，为挖掘街景语义信息、理解和定量表达场所物质空间、建成环境的特征提供了强有力的支持。在此背景下，街景影像被广泛应用于地理学、城市规划等众多领域，并在此过程中出现了大量的新方法和新视角，为基于大数据的城市环境研究、人地关系研究、空间数据挖掘与知识发现研究提供了新的思路。本文综述了基于街景影像和人工智能技术的相关研究，从深度学习和计算机视觉两个方面对街景影像分析的支撑技术进行了梳理，并从场所物质空间量化、场所感知以及场所语义推测3个方面对街景影像研究的应用方向进行了总结，对其在发展过程中面临的数据稀疏性问题、分析方法的严谨性问题进行了归纳，同时讨论了未来研究的方向。
Street view imagery is a promising and growing big geo-data that provides current and historical images in more than 200 countries for urban physical environment representation and audit. Such data not only describes the visual details of the urban physical environment but also contains information about urban functions
socioeconomics
and human dynamics. Street view imagery has the potential to complement new and traditional data
such as remote sensing imagery and social sensing data.
However
traditional digital image processing techniques for street view imagery handling are limited. Extracting rich semantic information from street view imagery efficiently has always been a challenging issue. Until recently
the development of artificial intelligence has led to numerous breakthroughs in image processing and machine learning. Indeed
the last few years have witnessed the fast development of deep learning and computer vision techniques
which facilitate the understanding of scene semantics from street view imagery and the quantitative representation of the urban physical and built environments. Many new applications
novel methods
and thoughts regarding street view imagery have emerged
covering research fields
such as geography
urban planning
urban design
urban economics
public health
environmental psychology
and energy. This trend has provided new perspectives for big geo-data-driven urban environment analysis
human–land relationship study
and spatial data mining and knowledge discovery.
To summarize this research trend
this paper reviews the recent works on urban physical environment analysis using street view imagery. The key supporting techniques for street view imagery analytics are discussed in terms of two dimensions: deep learning and computer vision. Deep learning has been applied recently to various computer vision tasks
such as image classification
image segmentation
and object detection. The success of deep learning techniques is attributed to their ability to learn rich high-level image representations as opposed to the hand-designed low-level features used in other image understanding methods.
Additionally
this paper summarizes the street view imagery applications in three aspects: place representation
sense of place
and place semantics reasoning. “Place representation” includes works that extract visual elements that constitute the urban physical environment; “sense of place” refers to works that use street view imagery to understand how people respond to their surrounding environments regarding perceptions and emotions; and “place semantics reasoning” refers to studies that attempt to infer and estimate invisible factors
socio-economics
demographics
and human dynamics from street view imagery.
More importantly
the issues of this research field
such as the spatio-temporal uniformity of street view image data
and the lack of solid workflow of data analytics
are highlighted.Finally
the development prospects of street view imagery are discussed. Crowdsourcing platforms and the field of the autonomous vehicle will increase the number of street view image sources. More issues
including how physical environments are involved and whether a universal law exists regarding the distribution of physical elements in space
are expected to be explored in future works.
Alvarez Leon L F and Quinn S . 2019 . The value of crowdsourced street-level imagery: examining the shifting property regimes of OpenStreetCam and Mapillary . GeoJournal , 84 ( 2 ): 395 - 414 [ DOI: 10.1007/s10708-018-9865-4 http://dx.doi.org/10.1007/s10708-018-9865-4 ]
Arietta S M , Efros A A , Ramamoorthi R and Agrawala M . 2014 . City forensics: using visual elements to predict non-visual city attributes . IEEE Transactions on Visualization and Computer Graphics , 20 ( 12 ): 2624 - 2633 [ DOI: 10.1109/TVCG.2014.2346446 http://dx.doi.org/10.1109/TVCG.2014.2346446 ]
Cao R , Zhu J S , Tu W , Li Q Q , Cao J Z , Liu B Z , Zhang Q and Qiu G P . 2018 . Integrating aerial and street view images for urban land use classification . Remote Sensing , 10 ( 10 ): 1553 [ DOI: 10.3390/rs10101553 http://dx.doi.org/10.3390/rs10101553 ]
Cordts M , Omran M , Ramos S , Rehfeld T , Enzweiler M , Benenson R , Franke U , Roth S and Schiele B . 2016 . The cityscapes dataset for semantic urban scene understanding // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, USA : IEEE : 3213 - 3223 [ DOI: 10.1109/CVPR.2016.350 http://dx.doi.org/10.1109/CVPR.2016.350 ]
Dalal N and Triggs B . 2005 . Histograms of oriented gradients for human detection // Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego, CA, USA : IEEE : 886 - 893 [ DOI: 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ]
Doersch C , Singh S , Gupta A , Sivic J and Efros A A . 2012 . What makes Paris look like Paris? ACM Transactions on Graphics , 31 ( 4 ): 101 [ DOI: 10.1145/2185520.2185597 http://dx.doi.org/10.1145/2185520.2185597 ]
Dubey A , Naik N , Parikh D , Raskar R and Hidalgo C A . 2016 . Deep learning the city: quantifying urban perception at a global scale // Proceedings of the 14th European Conference on Computer Vision – ECCV 2016 . Amsterdam, The Netherlands : Springer : 196 - 212 [ DOI: 10.1007/978-3-319-46448-0_12 http://dx.doi.org/10.1007/978-3-319-46448-0_12 ]
Everingham M , Van Gool L , Williams C K I , Winn J and Zisserman A . 2010 . The PASCAL Visual Object Classes (VOC) challenge . International Journal of Computer Vision , 88 ( 2 ): 303 - 338 [ DOI: 10.1007/s11263-009-0275-4 http://dx.doi.org/10.1007/s11263-009-0275-4 ]
Fu X , Jia T X , Zhang X Q , Li S L and Zhang Y L . 2019 . Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning. PLoS One , 14 ( 5 ): e 0217505 [ DOI: 10.1371/journal.pone.0217505 http://dx.doi.org/10.1371/journal.pone.0217505 ]
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 . Proceedings of the National Academy of Sciences of the United States of America , 114 ( 50 ): 13108 - 13113 [ DOI: 10.1073/pnas.1700035114 http://dx.doi.org/10.1073/pnas.1700035114 ]
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 . Building and Environment , 134 : 155 - 167 [ DOI: 10.1016/j.buildenv.2018.02.042 http://dx.doi.org/10.1016/j.buildenv.2018.02.042 ]
Gong P . 2019 . Towards more extensive and deeper application of remote sensing . Journal of Remote Sensing , 23 ( 4 ): 567 - 569
宫鹏 . 2019 . 对遥感科学应用的一点看法 . 遥感学报 , 23 ( 4 ): 567- 569 [ DOI: 10.11834/jrs.20199223 http://dx.doi.org/10.11834/jrs.20199223 ]
Hao X H and Long Y . 2017 . Street greenery: a new indicator for evaluating walkability . Shanghai Urban Planning Review , ( 1 ): 32 - 36, 49
郝新华 , 龙瀛 . 2017 . 街道绿化: 一个新的可步行性评价指标. 上海城市规划, (1): 32 - 36 , 49
He K M , Gkioxari G , Dollár P and Girshick R . 2017a . Mask R-CNN // Proceedings of the IEEE International Conference on Computer Vision . Venice, Italy : IEEE : 2980 - 2988 [ DOI: 10.1109/ICCV.2017.322 http://dx.doi.org/10.1109/ICCV.2017.322 ]
He K M , Zhang X Y , Ren S Q and Sun J . 2016 . Deep residual learning for image recognition // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, USA : IEEE : 770 - 778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
He L , Páez A and Liu D S . 2017b . Built environment and violent crime: an environmental audit approach using Google Street View . Computers, Environment and Urban Systems , 66 : 83 - 95 [ DOI: 10.1016/j.compenvurbsys.2017.08.001 http://dx.doi.org/10.1016/j.compenvurbsys.2017.08.001 ]
Helbich M , Yao Y , Liu Y , Zhang J B , Liu P H and Wang R Y . 2019 . Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China . Environment International , 126 : 107 - 117 [ DOI: 10.1016/j.envint.2019.02.013 http://dx.doi.org/10.1016/j.envint.2019.02.013 ]
Hinton G E and Salakhutdinov R R . 2006 . Reducing the dimensionality of data with neural networks . Science , 313 ( 5786 ): 504 - 507 [ DOI: 10.1126/science.1127647 http://dx.doi.org/10.1126/science.1127647 ]
Hu C B , Zhang F , Gong F Y , Ratti C and Li X . 2020 . Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning . Building and Environment , 167 : 106424 [ DOI: 10.1016/j.buildenv.2019.106424 http://dx.doi.org/10.1016/j.buildenv.2019.106424 ]
Huang G , Liu Z , Van Der Maaten L and Weinberger K Q . 2017 . Densely connected convolutional networks // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA : IEEE : 2261 - 2269 [ DOI: 10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]
Huang J X , Obracht-Prondzynska H , Kamrowska-Zaluska D , Sun Y M and Li L S . 2021 . The image of the City on social media: A comparative study using “Big Data” and “Small Data” methods in the Tri-City Region in Poland . Landscape and Urban Planning 206 : 103977 . [ DOI: 10.1016/j.landurbplan.2020.103977 http://dx.doi.org/10.1016/j.landurbplan.2020.103977 ]
Ibrahim M R , Haworth J and Cheng T . 2019 . URBAN-i: from urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision . Environment and Planning B : Urban Analytics and City Science [ DOI: 10.1177/2399808319846517 http://dx.doi.org/10.1177/2399808319846517 ]
Ilic L , Sawada M and Zarzelli A . 2019 . Deep mapping gentrification in a large Canadian city using deep learning and Google Street View . PLoS One , 14 ( 3 ): e 0212814 [ DOI: 10.1371/journal.pone.0212814 http://dx.doi.org/10.1371/journal.pone.0212814 ]
Kang H Y , Wang J , Wang Y , Angsuesser S and Fei T . 2017 . Mapping the sensitivity of the public emotion to the movement of stock market value: a case study of Manhattan // Proceedings of the International Archives of the Photogrammetry , Remote Sensing and Spatial Information Sciences. Wuhan : [ s .n.] [ DOI: 10.5194/isprs-archives-XLII-2-W7-1213-2017 http://dx.doi.org/10.5194/isprs-archives-XLII-2-W7-1213-2017 ]
Kang Y H , Zhang F , Gao S , Lin H and Liu Y . 2020 . A review of urban physical environment sensing using streat view imagery in public health studies . Annals of GIS , 26 ( 3 ): 261 - 275 [ DOI: 1475683.2020.1791954 http://dx.doi.org/1475683.2020.1791954 ]
Kang Y H , Jia Q Y , Gao S , Zeng X H , Wang Y Y , Angsuesser S , Liu Y , Ye X Y and Fei T . 2019 . Extracting human emotions at different places based on facial expressions and spatial clustering analysis . Transactions in GIS , 23 ( 3 ): 450 - 480 [ DOI: 10.1111/tgis.12552 http://dx.doi.org/10.1111/tgis.12552 ]
Khosla A , An B , Lim J J and Torralba A . 2014 . Looking beyond the visible scene // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Columbus, OH, USA : IEEE : 3710 - 3717 [ DOI: 10.1109/CVPR.2014.474 http://dx.doi.org/10.1109/CVPR.2014.474 ]
Krizhevsky A , Sutskever I and Hinton G E . 2012 . ImageNet classification with deep convolutional neural networks // Proceedings of the 25th International Conference on Neural Information Processing Systems . Lake Tahoe, Nevada : ACM : 1097 - 1105
Law S , Paige B and Russell C . 2018 . Take a look around: using street view and satellite images to estimate house prices . arXiv : 1807 . 07155
Law S , Seresinhe C I , Shen Y and Gutierrez-Roig M . 2020 . Street-Frontage-Net: urban image classification using deep convolutional neural networks . International Journal of Geographical Information Science , 34 ( 4 ): 681 - 707 [ DOI: 10.1080/13658816.2018.1555832 http://dx.doi.org/10.1080/13658816.2018.1555832 ]
LeCun Y , Bengio Y and Hinton G . 2015 . Deep learning . Nature , 521 ( 7553 ): 436 - 444 [ DOI: 10.1038/nature14539 http://dx.doi.org/10.1038/nature14539 ]
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 . Transportation Research Part C : Emerging Technologies , 106 : 132 - 144 [ DOI: 10.1016/j.trc.2019.07.013 http://dx.doi.org/10.1016/j.trc.2019.07.013 ]
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 and Urban Planning , 191 : 103387 [ DOI: 10.1016/j.landurbplan.2018.07.011 http://dx.doi.org/10.1016/j.landurbplan.2018.07.011 ]
Li X J , Ratti C and Seiferling I . 2018 . Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google Street View . Landscape and Urban Planning , 169 : 81 - 91 [ DOI: 10.1016/j.landurbplan.2017.08.011 http://dx.doi.org/10.1016/j.landurbplan.2017.08.011 ]
Li X J , Zhang C R and Li W D . 2017 . Building block level urban land-use information retrieval based on Google Street View images . GIScience and Remote Sensing , 54 ( 6 ): 819 - 835 [ DOI: 10.1080/15481603.2017.1338389 http://dx.doi.org/10.1080/15481603.2017.1338389 ]
Li X J , Zhang C R , Li W D , Ricard R , Meng Q Y and Zhang W X . 2015 . Assessing street-level urban greenery using Google Street View and a modified green view index . Urban Forestry and Urban Greening , 14 ( 3 ): 675 - 685 [ DOI: 10.1016/j.ufug.2015.06.006 http://dx.doi.org/10.1016/j.ufug.2015.06.006 ]
Lin T Y , Maire M , Belongie S , Hays J , Perona P , Ramanan D , Dollár P and Zitnick C L . 2014 . Microsoft COCO: common objects in context // Proceedings of the 13th European Conference on Computer Vision . Zurich, Switzerland : Springer : 740 - 755 [ DOI: 10.1007/978-3-319-10602-1_48 http://dx.doi.org/10.1007/978-3-319-10602-1_48 ]
Liu L , Silva E A , Wu C Y and Wang H . 2017 . A machine learning-based method for the large-scale evaluation of the qualities of the urban environment . Computers, Environment and Urban Systems , 65 : 113 - 125 [ DOI: 10.1016/j.compenvurbsys.2017.06.003 http://dx.doi.org/10.1016/j.compenvurbsys.2017.06.003 ]
Liu L , Zhou B L , Zhao J H and Ryan B D . 2016a . C-IMAGE: city cognitive mapping through geo-tagged photos . GeoJournal , 81 ( 6 ): 817 - 861 [ DOI: 10.1007/s10708-016-9739-6 http://dx.doi.org/10.1007/s10708-016-9739-6 ]
Liu W , Anguelov D , Erhan D , Szegedy C , Reed S , Fu C Y and Berg A C . 2016b . SSD: single shot MultiBox detector // Proceedings of the 14th European Conference on Computer Vision . Amsterdam, The Netherlands : Springer : 21 - 37 [ DOI: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ]
Liu Y , Liu X , Gao S , Gong L , Kang C G , Zhi Y , Chi G H and Shi L . 2015 . Social sensing: a new approach to understanding our socioeconomic environments . Annals of the Association of American Geographers , 105 ( 3 ): 512 - 530 [ DOI: 10.1080/00045608.2015.1018773 http://dx.doi.org/10.1080/00045608.2015.1018773 ]
Liu Z Y , Yang A Q , Gao M Y , Jiang H , Kang Y H , Zhang F and Fei T . 2019 . Towards feasibility of photovoltaic road for urban traffic-solar energy estimation using street view image . Journal of Cleaner Production , 228 : 303 - 318 [ DOI: 10.1016/j.jclepro.2019.04.262 http://dx.doi.org/10.1016/j.jclepro.2019.04.262 ]
Long Y and Liu L . 2017 . How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View. PLoS One , 12 ( 2 ): e 0171110 [ DOI: 10.1371/journal.pone.0171110 http://dx.doi.org/10.1371/journal.pone.0171110 ]
Long Y and Ye Y . 2019 . Measuring human-scale urban form and its performance . Landscape and Urban Planning , 191 : 103612 [ DOI: 10.1016/j.landurbplan.2019.103612 http://dx.doi.org/10.1016/j.landurbplan.2019.103612 ]
Long Y and Zhou Y . 2017 . Pictorial urbanism: a new approach for human scale urban morphology study . Planners , 33 ( 2 ): 54 - 60
龙瀛 , 周垠 . 2017 . 图片城市主义: 人本尺度城市形态研究的新思路 . 规划师 , 33 ( 2 ): 54- 60 [ DOI: 10.3969/j.issn.1006-0022.2017.02.009 http://dx.doi.org/10.3969/j.issn.1006-0022.2017.02.009 ]
Lu Y , Yang Y Y , Sun G B and Gou Z H . 2019 . Associations between overhead-view and eye-level urban greenness and cycling behaviors . Cities , 88 : 10 - 18 [ DOI: 10.1016/j.cities.2019.01.003 http://dx.doi.org/10.1016/j.cities.2019.01.003 ]
Ma R X , Wang W , Zhang F , Shim K and Ratti C . 2019 . Typeface reveals spatial economical patterns . Scientific Reports , 9 ( 1 ): 15946 [ DOI: 10.1038/s41598-019-52423-y http://dx.doi.org/10.1038/s41598-019-52423-y ]
Ma X Y , Ma C Y , Wu C , Xi Y L , Yang R F , Peng N Y Z , Zhang C and Ren F . 2021 . Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing . Cities 110 : 103086 . [ DOI: 10.1016/j.cities.2020.103086 http://dx.doi.org/10.1016/j.cities.2020.103086 ]
Naik N , Kominers S D , Raskar R , Glaeser E L and Hidalgo C A . 2017 . Computer vision uncovers predictors of physical urban change . Proceedings of the National Academy of Sciences of the United States of America , 114 ( 29 ): 7571 - 7576 [ DOI: 10.1073/pnas.1619003114 http://dx.doi.org/10.1073/pnas.1619003114 ]
Naik N , Philipoom J , Raskar R and Hidalgo C . 2014 . Streetscore -- predicting the perceived safety of one million streetscapes // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . Columbus, OH, USA : IEEE : 793 - 799 [ DOI: 10.1109/CVPRW.2014.121 http://dx.doi.org/10.1109/CVPRW.2014.121 ]
Nair V and Hinton G E . 2010 . Rectified linear units improve restricted Boltzmann machines // Proceedings of the 27th International Conference on International Conference on Machine Learning . Madison, WI, United States : ACM : 807 - 814
Oliva A and Torralba A . 2001 . Modeling the shape of the scene: a holistic representation of the spatial envelope . International Journal of Computer Vision , 42 ( 3 ): 145 - 175 [ DOI: 10.1023/A:1011139631724 http://dx.doi.org/10.1023/A:1011139631724 ]
Patterson G and Hays J . 2012 . Sun attribute database : discovering, annotating, and recognizing scene attributes// Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition . Providence, RI, USA : IEEE : 2751 - 2758 [ DOI: 10.1109/CVPR.2012.6247998 http://dx.doi.org/10.1109/CVPR.2012.6247998 ]
Pred A . 1984 . Place as historically contingent process: structuration and the time-geography of becoming places . Annals of the Association of American Geographers , 74 ( 2 ): 279 - 297 [ DOI: 10.1111/j.1467-8306.1984.tb01453.x http://dx.doi.org/10.1111/j.1467-8306.1984.tb01453.x ]
Quercia D , O’Hare N K and Cramer H . 2014 . Aesthetic capital : what makes london look beautiful, quiet, and happy?// Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing . Maryland, Baltimore, USA : ACM : 945 - 955 [ DOI: 10.1145/2531602.2531613 http://dx.doi.org/10.1145/2531602.2531613 ]
Ren S Q , He K M , Girshick R and Sun J . 2015 . Faster R-CNN: towards real-time object detection with region proposal networks // Proceedings of the 28th International Conference on Neural Information Processing Systems . Cambridge, MA, USA : ACM : 91 - 99
Roda C , Charreire H , Feuillet T , Mackenbach J D , Compernolle S , Glonti K , Ben Rebah M , Bárdos H , Rutter H , McKee M , De Bourdeaudhuij I , Brug J , Lakerveld J B J and Oppert J M . 2016 . Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods . Obesity Reviews , 17 ( S1 ): 31 - 41 [ DOI: 10.1111/obr.12376 http://dx.doi.org/10.1111/obr.12376 ]
Rumelhart D E , Hinton G E and Williams R J . 1986 . Learning representations by back-propagating errors . Nature , 323 ( 6088 ): 533 - 536 [ DOI: 10.1038/323533a0 http://dx.doi.org/10.1038/323533a0 ]
Russakovsky O , Deng J , Su H , Krause J , Satheesh S , Ma S , Huang Z H , Karpathy A , Khosla A , Bernstein M , Berg A C and Li F F . 2015 . ImageNet large scale visual recognition challenge . International Journal of Computer Vision , 115 ( 3 ): 211 - 252 [ DOI: 10.1007/s11263-015-0816-y http://dx.doi.org/10.1007/s11263-015-0816-y ]
Salesses P , Schechtner K and Hidalgo C A . 2013 . The collaborative image of the city: mapping the inequality of urban perception . PLoS One , 8 ( 7 ): e 68400 [ DOI: 10.1371/journal.pone.0119352 http://dx.doi.org/10.1371/journal.pone.0119352 ]
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 and Urban Planning , 165 : 93 - 101 [ DOI: 10.1016/j.landurbplan.2017.05.010 http://dx.doi.org/10.1016/j.landurbplan.2017.05.010 ]
Seresinhe C I , Preis T and Moat H S . 2017 . Using deep learning to quantify the beauty of outdoor places . Royal Society Open Science , 4 ( 7 ): 170170 [ DOI: 10.1098/rsos.170170 http://dx.doi.org/10.1098/rsos.170170 ]
Simonyan K and Zisserman A . 2014 . Very deep convolutional networks for large-scale image recognition . arXiv preprint arXiv : 1409 . 1556
Srivastava S , Muñoz J E V , Lobry S and Tuia D . 2020 . Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data . International Journal of Geographical Information Science , 34 ( 6 ): 1117 - 1136 [ DOI: 10.1080/13658816.2018.1542698 http://dx.doi.org/10.1080/13658816.2018.1542698 ]
Su S L , Zhou H , Xu M Y , Ru H , Wang W and Weng M . 2019 . Auditing street walkability and associated social inequalities for planning implications . Journal of Transport Geography , 74 : 62 - 76 [ DOI: 10.1016/j.jtrangeo.2018.11.003 http://dx.doi.org/10.1016/j.jtrangeo.2018.11.003 ]
Suel E , Polak J W , Bennett J E and Ezzati M . 2019 . Measuring social, environmental and health inequalities using deep learning and street imagery . Scientific Reports , 9 ( 1 ): 6229 [ DOI: 10.1038/s41598-019-42036-w http://dx.doi.org/10.1038/s41598-019-42036-w ]
Szegedy C , Liu W , Jia Y Q , Sermanet P , Reed S , Anguelov D , Erhan D , Vanhoucke V and Rabinovich A . 2015 . Going deeper with convolutions // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Boston, MA, USA : IEEE : 1 - 9 [ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]
Tang J X and Long Y . 2019 . Measuring visual quality of street space and its temporal variation: methodology and its application in the Hutong area in Beijing . Landscape and Urban Planning , 191 : 103436 [ DOI: 10.1016/j.landurbplan.2018.09.015 http://dx.doi.org/10.1016/j.landurbplan.2018.09.015 ]
Wang R , Chen H S , Liu Y , Lu Y and Yao Y . 2019a . Neighborhood social reciprocity and mental health among older adults in China: the mediating effects of physical activity, social interaction, and volunteering . BMC Public Health , 19 ( 1 ): 1036 [ DOI: 10.1186/s12889-019-7385-x http://dx.doi.org/10.1186/s12889-019-7385-x ]
Wang R Y , Helbich M , Yao Y , Zhang J B , Liu P H , Yuan Y and Liu Y . 2019b . Urban greenery and mental wellbeing in adults: cross-sectional mediation analyses on multiple pathways across different greenery measures . Environmental Research , 176 : 108535 [ DOI: 10.1016/j.envres.2019.108535 http://dx.doi.org/10.1016/j.envres.2019.108535 ]
Wang R Y , Liu Y , Lu Y , Zhang J B , Liu P H , Yao Y and Grekousis G . 2019c . Perceptions of built environment and health outcomes for older Chinese in Beijing: a big data approach with street view images and deep learning technique . Computers, Environment and Urban Systems , 78 : 101386 [ DOI: 10.1016/j.compenvurbsys.2019.101386 http://dx.doi.org/10.1016/j.compenvurbsys.2019.101386 ]
Wang W S , Yang S , He Z Y , Wang M J , Zhang J L and Zhang W S . 2018 . Urban perception of commercial activeness from satellite images and streetscapes // Proceedings of Companion of the Web Conference 2018 . ACM Press, Lyon, France : ACM : 647 - 654 [ DOI: 10.1145/3184558.3186581 http://dx.doi.org/10.1145/3184558.3186581 ]
Wang Z T , Liang Q H , Duarte F , Zhang F , Charron L , Johnsen L , Cai B and Ratti C . 2019d . Quantifying legibility of indoor spaces using Deep Convolutional Neural Networks: case studies in train stations . Building and Environment , 160 : 106099 [ DOI: 10.1016/j.buildenv.2019.04.035 http://dx.doi.org/10.1016/j.buildenv.2019.04.035 ]
Wilson J Q and Kelling G L . 1982 . Broken windows . The Atlantic Monthly , 249 : 29 - 38
Ye Y , Zeng W , Shen Q M , Zhang X H and Lu Y . 2019 . The visual quality of streets: a human-centred continuous measurement based on machine learning algorithms and street view images . Environment and Planning B : Urban Analytics and City Science , 46 ( 8 ): 1439 - 1457 [ DOI: 10.1177/2399808319828734 http://dx.doi.org/10.1177/2399808319828734 ]
Yin L and Wang Z X . 2016 . Measuring visual enclosure for street walkability: using machine learning algorithms and Google Street View imagery . Applied Geography , 76 : 147 - 153 [ DOI: 10.1016/j.apgeog.2016.09.024 http://dx.doi.org/10.1016/j.apgeog.2016.09.024 ]
Yoshimura Y , Cai B , Wang Z T and Ratti C . 2019 . Deep learning architect: classification for architectural design through the eye of artificial intelligence //Geertman S, Zhan Q M, Allan A and Pettit C, eds. Computational Urban Planning and Management for Smart Cities . Cham : Springer : 249 - 265 [ DOI: 10.1007/978-3-030-19424-6_14 http://dx.doi.org/10.1007/978-3-030-19424-6_14 ]
Zhang F , Duarte F , Ma R X , Milioris D , Lin H and Ratti C . 2016 . Indoor space recognition using deep convolutional neural network: a case study at MIT campus . arXiv : 1610 . 02414
Zhang F , Fan Z Y , Kang Y H , Hu Y J and Carlo Ratti . 202 . “ Perception bias”: Deciphering a mismatch between urban crime and perception of safety . Landscape and Urban Planning 207 : 104003 . [ DOI: 10.1016/j.landurbplan.2020.104003 http://dx.doi.org/10.1016/j.landurbplan.2020.104003 ]
Zhang F , Hu M Y , Che W T , Lin H and Fang C Y . 2018a . Framework for virtual cognitive experiment in virtual geographic environments . ISPRS International Journal of Geo-Information , 7 ( 1 ): 36 [ DOI: 10.3390/ijgi7010036 http://dx.doi.org/10.3390/ijgi7010036 ]
Zhang F , Wu L , Zhu D and Liu Y . 2019a . Social sensing from street-level imagery: a case study in learning spatio-temporal urban mobility patterns . ISPRS Journal of Photogrammetry and Remote Sensing , 153 : 48 - 58 [ DOI: 10.1016/j.isprsjprs.2019.04.017 http://dx.doi.org/10.1016/j.isprsjprs.2019.04.017 ]
Zhang F , Zhang D , Liu Y and Lin H . 2018b . Representing place locales using scene elements . Computers, Environment and Urban Systems , 71 : 153 - 164 [ DOI: 10.1016/j.compenvurbsys.2018.05.005 http://dx.doi.org/10.1016/j.compenvurbsys.2018.05.005 ]
Zhang F , Zhou B L , Liu L , Liu Y , Fung H H , Lin H and Ratti C . 2018c . Measuring human perceptions of a large-scale urban region using machine learning . Landscape and Urban Planning , 180 : 148 - 160 [ DOI: 10.1016/j.landurbplan.2018.08.020 http://dx.doi.org/10.1016/j.landurbplan.2018.08.020 ]
Zhang F , Zhou B L , Ratti C and Liu Y . 2019b . Discovering place-informative scenes and objects using social media photos . Royal Society Open Science , 6 ( 3 ): 181375 [ DOI: 10.1098/rsos.181375 http://dx.doi.org/10.1098/rsos.181375 ]
Zhang F , Zu J Y , Hu M Y , Zhu D , Kang Y H , Gao S , Zhang Y and Huang Z . 2020 . Uncovering inconspicuous places using social media check-ins and street view images . Computers, Environment and Urban Systems 81 : 101478 . [ DOI: 10.1016/j.compenvurbsys.2020.101478 http://dx.doi.org/10.1016/j.compenvurbsys.2020.101478 ]
Zhang L Y , Pei T , Chen Y J , Song C and Liu X Q . 2019 . A review of urban environmental assessment based on street view images . Journal of Geo-information Science , 21 ( 1 ): 46 - 58
张丽英 , 裴韬 , 陈宜金 , 宋辞 , 刘小茜 . 2019 . 基于街景图像的城市环境评价研究综述 . 地球信息科学学报 , 21 ( 1 ): 46- 58 [ DOI: 10.12082/dqxxkx.2019.180311 http://dx.doi.org/10.12082/dqxxkx.2019.180311 ]
Zhao H S , Shi J P , Qi X J , Wang X G and Jia J Y . 2017 . Pyramid scene parsing network // Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA : IEEE : 6230 - 6239 [ DOI: 10.1109/CVPR.2017.660 http://dx.doi.org/10.1109/CVPR.2017.660 ]
Zhou B L , Lapedriza A , Khosla A , Oliva A and Torralba A . 2018 . Places: a 10 million image database for scene recognition . IEEE Transactions on Pattern Analysis and Machine Intelligence , 40 ( 6 ): 1452 - 1464 [ DOI: 10.1109/TPAMI.2017.2723009 http://dx.doi.org/10.1109/TPAMI.2017.2723009 ]
Zhou B L , Lapedriza A , Xiao J X , Torralba A and Oliva A . 2014 . Learning deep features for scene recognition using places database // Proceedings of the 27th International Conference on Neural Information Processing Systems . Montreal, Canada : ACM : 487 - 495
Zhou B L , Zhao H , Puig X , Xiao T T , Fidler S , Barriuso A and Torralba A . 2019 . Semantic understanding of scenes through the ADE20K dataset . International Journal of Computer Vision , 127 ( 3 ): 302 - 321 [ DOI: 10.1007/s11263-018-1140-0 http://dx.doi.org/10.1007/s11263-018-1140-0 ]
Zhu D , Zhang F , Wang S Y , Wang Y L , Cheng X M , Huang Z and Liu Y . 2020 . Understanding place characteristics in geographic contexts through graph convolutional neural networks . Annals of the American Association of Geographers , 110 ( 2 ): 408 - 420 [ DOI: 10.1080/24694452.2019.1694403 http://dx.doi.org/10.1080/24694452.2019.1694403 ]
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