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This scoping review dataset on the use of panoramic street-level imagery in urban research was compiled for the following article: Cinnamon J., and Jahiu L. (2021). Panoramic street-level imagery in data-driven urban research: A comprehensive global review of applications, techniques, and practical considerations. ISPRS International Journal of Geo-Information 10(7), https://doi.org/10.3390/ijgi10070471
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Literature Record (author, date and publication details)
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Acharya et al. (2017) Neighborhood Watch: Using CNNs to Predict Income Brackets from Google Street View Images. http://cs231n.stanford.edu/reports/2017/pdfs/556.pdf
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Alhasoun, F., & Gonzalez, M. (2019). Urban street contexts classification using convolutional neural networks and streets imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 1198-1204). IEEE. doi:10.1109/ICMLA.2019.00198
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Araujo, A. A., Sampaio, J. C., Evangelista, R. S., Mantuan, A. B., & Fernandes, L. A. F. (2015). Accurate location of façades of interest in street view panoramic sequences. In 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images (pp. 281-288). IEEE.doi:10.1109/SIBGRAPI.2015.32
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Babahajiani et al 2017 Urban 3D segmentation and modelling from street view images and LiDAR point clouds Machine Vision and Applications
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Bader, M. D. M., Mooney, S. J., Lee, Y. J., Sheehan, D., Neckerman, K. M., Rundle, A. G., & Teitler, J. O. (2015). Development and deployment of the computer assisted neighborhood visual assessment system (CANVAS) to measure health-related neighborhood conditions. Health & Place, 31, 163-172. https://doi.org/10.1016/j.healthplace.2014.10.012
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Balali, V., Ashouri Rad, A., & Golparvar-Fard, M. (2015). Detection, classification, and mapping of U.S. traffic signs using google street view images for roadway inventory management. Visualization in Engineering, 3(1), 1-18. doi:10.1186/s40327-015-0027-1
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Bates, C. J. (2014). Systematic social observation with virtual imagery: Urban density and residential burglary. Thesis
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Ben-Joseph, E., Lee, J. S., Cromley, E. K., Laden, F., & Troped, P. J. (2013). Virtual and actual: Relative accuracy of on-site and web-based instruments in auditing the environment for physical activity. Health & Place, 19, 138-150. https://doi.org/10.1016/j.healthplace.2012.11.001
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Berland, A., & Lange, D. A. (2017). Google street view shows promise for virtual street tree surveys. Urban Forestry & Urban Greening, 21, 11-15. doi:10.1016/j.ufug.2016.11.006
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Berland, A., Roman, L. A., & Vogt, J. (2019). Can field crews telecommute? varied data quality from citizen science tree inventories conducted using street-level imagery. Forests, 10(4), 349. doi:10.3390/f10040349
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Bethlehem et al. (2014). The SPOTLIGHT virtual audit tool: A valid and reliable tool to assess obesogenic characteristics of the built environment. International Journal of Health Geographics, 13(1), 52-52. doi:10.1186/1476-072x-13-52
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Boller, D., Moy de Vitry, M., D. Wegner, J., & Leitão, J. P. (2019). Automated localization of urban drainage infrastructure from public-access street-level images. Urban Water Journal, 16(7), 480-493.
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Branson, S., Wegner, J. D., Hall, D., Lang, N., Schindler, K., & Perona, P. (2018). From google maps to a fine-grained catalog of street trees. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 13-30. doi:10.1016/j.isprsjprs.2017.11.008
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Brazil, W., O'Dowd, A., & Caulfield, B. (2017). Using eye-tracking technology and google street view to understand cyclists' perceptions. Paper presented at the 1-6. doi:10.1109/ITSC.2017.8317619
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Brookfield, K., & Tilley, S. (2016). Using virtual street audits to understand the walkability of older adults' route choices by gender and age. International Journal of Environmental Research and Public Health, 13(11), 1061. doi:10.3390/ijerph13111061
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Cai, B., Li, X., & Ratti, C. (2019). Quantifying urban canopy cover with deep convolutional neural networks.
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Cao, R., Zhu, J., Tu, W., Li, Q., Cao, J., Liu, B., . . . Qiu, G. (2018). Integrating aerial and street view images for urban land use classification. Remote Sensing (Basel, Switzerland), 10(10), 1553. doi:10.3390/rs10101553
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Carrasco-Hernandez, R., Smedley, A. R. D., & Webb, A. R. (2015). Using urban canyon geometries obtained from google street view for atmospheric studies: Potential applications in the calculation of street level total shortwave irradiances. Energy and Buildings, 86, 340-348. doi:10.1016/j.enbuild.2014.10.001
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Cetiner, B. (2020). Image-based modeling of bridges and its applications to evaluating resiliency of transportation networks. Thesis
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Chang, S., Wang, Z., Mao, D., Guan, K., Jia, M., & Chen, C. (2020). Mapping the essential urban land use in changchun by applying random forest and multi-source geospatial data. Remote Sensing (Basel, Switzerland), 12(15), 2488. doi:10.3390/rs12152488
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Chen, J., Zhou, C., & Li, F. (2020). Quantifying the green view indicator for assessing urban greening quality: An analysis based on internet-crawling street view data. Ecological Indicators, 113, 106192. doi:10.1016/j.ecolind.2020.106192
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Chen, L., Lu, Y., Sheng, Q., Ye, Y., Wang, R., & Liu, Y. (2020). Estimating pedestrian volume using street view images: A large-scale validation test. Computers, Environment and Urban Systems, 81, 101481. doi:10.1016/j.compenvurbsys.2020.101481
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Chen, Liujia; Yao, Xiaojing; Liu, Yalan; Zhu, Yujiao; Chen, Wei; Zhao, Xizhi; Chi, Tianhe. 2020. "Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China" ISPRS Int. J. Geo-Inf. 9, no. 2: 106. https://doi.org/10.3390/ijgi9020106
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Chen, X., Meng, Q., Hu, D., Zhang, L., & Yang, J. (2019). Evaluating greenery around streets using baidu panoramic street view images and the panoramic green view index. Forests, 10(12), 1109. doi:10.3390/f10121109
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Chen, Y. (2018). How green are residential areas? An analysis of community greening emerging multi-source geo-data.
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Chen, Y. (2019). Analyzing determinants of urban vibrancy: A big data approach on connecting built environment, social activity, and images of places. Thesis
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Cheng, L., Chu, S., Zong, W., Li, S., Wu, J., & Li, M. (2017). Use of tencent street view imagery for visual perception of streets. ISPRS International Journal of Geo-Information, 6(9), 265. doi:10.3390/ijgi6090265
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Choiri 2017 Quantifying and Predicting Urban Attractiveness with Street-View Data and Convolutional Neural Networks. Thesis
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Christman, Z. J., Wilson-Genderson, M., Heid, A., & Pruchno, R. (2020). The effects of neighborhood built environment on walking for leisure and for purpose among older people. The Gerontologist, 60(4), 651-660. doi:10.1093/geront/gnz093
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Chudyk, A. M., Winters, M., Gorman, E., McKay, H. A., & Ashe, M. C. (2014). Agreement between virtual and in-the-field environment audits of assisted living sites. Journal of Aging and Physical Activity, 22(3), 414-420. https://doi.org/10.1123/JAPA.2013-0047
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Clarke, P., Ailshire, J., Melendez, R., Bader, M., & Morenoff, J. (2010). Using google earth to conduct a neighborhood audit: Reliability of a virtual audit instrument. Health & Place, 16(6), 1224-1229. https://doi.org/10.1016/j.healthplace.2010.08.007
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Clews, C., Brajkovich-Payne, R., Dwight, E., Fauzul, A. A., Burton, M., Carleton, O., ... & Thomson, G. (2016). Alcohol in urban streetscapes: a comparison of the use of Google Street View and on-street observation. BMC Public Health, 16(1), 1-8.
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Cohen, N., Chrobok, M., & Caruso, O. (2020). Google-truthing to assess hot spots of food retail change: A repeat cross-sectional street view of food environments in the bronx, new york. Health & Place, 62, 102291-102291. doi:10.1016/j.healthplace.2020.102291
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Curtis, J. W., Curtis, A., Mapes, J., Szell, A. B., & Cinderich, A. (2013). Using google street view for systematic observation of the built environment: analysis of spatio-temporal instability of imagery dates. International journal of health geographics, 12(1), 53.
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De Nadai, M., Vieriu, R. L., Zen, G., Dragicevic, S., Naik, N., Caraviello, M., . . . Lepri, B. (2016). Are safer looking neighborhoods more lively? A multimodal investigation into urban life.
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Deng, Z. (2019). Detect traffic signs from large street view images with deep learning. Thesis
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Diou, C., Lelekas, P., & Delopoulos, A. (2018). Image-based surrogates of socio-economic status in urban neighborhoods using deep multiple instance learning. Journal of Imaging, 4(11), 125.
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Doersch, C., Singh, S., Gupta, A., Sivic, J., & Efros, A. (2012). What makes paris look like paris? ACM Transactions on Graphics, 31(4), 1-9. doi:10.1145/2185520.2185597
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Dong, R., Zhang, Y., & Zhao, J. (2018). How green are the streets within the sixth ring road of beijing? an analysis based on tencent street view pictures and the green view index. International Journal of Environmental Research and Public Health, 15(7), 1367. doi:10.3390/ijerph15071367
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Du, K., Ning, J., & Yan, L. (2020). How long is the sun duration in a street canyon? —— analysis of the view factors of street canyons. Building and Environment, 172, 106680. doi:10.1016/j.buildenv.2020.106680
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Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016). Deep learning the city : Quantifying urban perception at A global scale.
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Egli, V., Zinn, C., Mackay, L., Donnellan, N., Villanueva, K., Mavoa, S., . . . Smith, M. (2019). Viewing obesogenic advertising in children's neighbourhoods using google street view. Geographical Research, 57(1), 84-97. doi:10.1111/1745-5871.12291
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Ewing, R., Hajrasouliha, A., Neckerman, K. M., Purciel-Hill, M., & Greene, W. (2016). Streetscape features related to pedestrian activity. Journal of Planning Education and Research, 36(1), 5-15.
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Feng Si-Yuan, WEI Ya-Nan, WANG Zhen-Juan, YU Xin-Yang. Pedestrian-view urban street vegetation monitoring using Baidu Street View images. Chin J Plant Ecol, 2020, 44(3): 205-213.
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Feuillet, T., Charreire, H., Roda, C., Ben Rebah, M., Mackenbach, J. D., Compernolle, S., . . . Oppert, J. -. (2016). Neighbourhood typology based on virtual audit of environmental obesogenic characteristics. Obesity Reviews, 17(S1), 19-30. doi:10.1111/obr.12378
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Fu, K., Chen, Z., & Lu, Y. (2018). StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception, 1-10. doi: : 10.1145/3274895.3274975
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Fu, X., Jia, T., Zhang, X., Li, S., & Zhang, Y. (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), e0217505-e0217505. doi:10.1371/journal.pone.0217505
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Gao, J., Wu, Z., Chen, J., & Chen, W. (2020). Beyond the bid‐rent: Two tales of land use transition in contemporary china. Growth and Change, 51(3), 1336-1356. doi:10.1111/grow.12408
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Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (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, 114(50), 13108-13113.
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Ghouaiel, N., & Lefèvre, S. (2016). Coupling ground-level panoramas and aerial imagery for change detection. Geo-Spatial Information Science, 19(3), 222-232. doi:10.1080/10095020.2016.1244998
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Glaeser, E. L., Kominers, S. D., Luca, M., & Naik, N. (2018). big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry, 56(1), 114-137. https://doi.org/10.1111/ecin.12364
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Goel, R., Garcia, L. M., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., ... & Woodcock, J. (2018). Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain. PloS one, 13(5).
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Gong, F., Zeng, Z., Ng, E., & Norford, L. K. (2019). Spatiotemporal patterns of street-level solar radiation estimated using google street view in a high-density urban environment. Building and Environment, 148, 547-566. doi:10.1016/j.buildenv.2018.10.025
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Gong, Z., Ma, Q., Kan, C., & Qi, Q. (2019). Classifying street spaces with street view images for a spatial indicator of urban functions. Sustainability (Basel, Switzerland), 11(22), 6424. doi:10.3390/su11226424
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Griew, P., Hillsdon, M., Foster, C., Coombes, E., Jones, A., & Wilkinson, P. (2013). Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 103.
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Grubesic, T. H., Wallace, D., Chamberlain, A. W., & Nelson, J. R. (2018). Using unmanned aerial systems (UAS) for remotely sensing physical disorder in neighborhoods. Landscape and Urban Planning, 169, 148-159.
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Gu, P., Han, Z., Cao, Z., Chen, Y., & Jiang, Y. (2018). Using open source data to measure street walkability and bikeability in china: A case of four cities. Transportation Research Record, 2672(31), 63-75. doi:10.1177/0361198118758652
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Gullón, P., Badland, H. M., Alfayate, S., Bilal, U., Escobar, F., Cebrecos, A., . . . Franco, M. (2015). Assessing walking and cycling environments in the streets of madrid: Comparing on-field and virtual audits. Journal of Urban Health, 92(5), 923-939. doi:10.1007/s11524-015-9982-z
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Guo, Z. (2013). Home parking convenience, household car usage, and implications to residential parking policies. Transport policy, 29, 97-106.
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Guo, Z. (2013). Residential street parking and car ownership: A study of households with off-street parking in the new york city region. Journal of the American Planning Association, 79(1), 32-48. doi:10.1080/01944363.2013.790100
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Han, A. T., Laurian, L., & Go, M. H. (2020). Transforming incinerators into community amenities? the seoul experience. Journal of Environmental Planning and Management, 63(8), 1427-1452. https://doi.org/10.1080/09640568.2019.1670626
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Hanibuchi, T., Nakaya, T., & Inoue, S. (2019). Virtual audits of streetscapes by crowdworkers. Health & Place, 59, 102203-102203. doi:10.1016/j.healthplace.2019.102203
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Hanson, C. S., Noland, R. B., & Brown, C. (2013). The severity of pedestrian crashes: an analysis using Google Street View imagery. Journal of transport geography, 33, 42-53.
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Hara, K., Azenkot, S., Campbell, M., Bennett, C. L., Le, V., Pannella, S., ... & Froehlich, J. E. (2015). Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with google street view: An extended analysis. ACM Transactions on Accessible Computing (TACCESS), 6(2), 1-23.
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Hara, K., Le, V., & Froehlich, J. (2013, April). Combining crowdsourcing and google street view to identify street-level accessibility problems. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 631-640).
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Hara, K., Sun, J., Moore, R., Jacobs, D., & Froehlich, E., J. (2014). Tohme: Detecting Curb Ramps in Google Street View Using Crowdsourcing, Computer Vision, and Machine Learning
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He, L., Páez, A., & Liu, D. (2017). 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
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Helbich, M., Yao, Y., Liu, Y., Zhang, J., Liu, P., & Wang, R. (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
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Hershey, D., & Wolfe, B. Recognizing Cities from Street View Images. Stanford University. 2016
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Hong, S.-Y. (2020). Linguistic Landscapes on Street-Level Images. ISPRS International Journal of Geo-Information, 9, 57.
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Hu, F., Liu, W., Lu, J., Song, C., Meng, Y., Wang, J., & Xing, H. (2020). Urban function as a new perspective for adaptive street quality assessment. Sustainability (Basel, Switzerland), 12(4), 1296. doi:10.3390/su12041296
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Huang, D., Brien, A., Omari, L., Culpin, A., Smith, M., & Egli, V. (2020). Bus stops near schools advertising junk food and sugary drinks. Nutrients, 12(4), 1192. doi:10.3390/nu12041192
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Huang, Z., Qi, H., Kang, C., Su, Y., & Liu, Y. (2020). An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data. Remote Sensing (Basel, Switzerland), 12(3254), 3254. doi:10.3390/rs12193254
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Hwang, S. W., & Kim, H. (2020). The intensifying gated exclusiveness of apartment complex boundary design in seoul, korea. Planning Perspectives, 35(4), 719-729. doi:10.1080/02665433.2020.1770623
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Hyam, R. (2017). Automated image sampling and classification can be used to explore perceived naturalness of urban spaces. PloS One, 12(1), e0169357-e0169357. doi:10.1371/journal.pone.0169357
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Iannelli, G. C., & Dell’Acqua, F. (2017). Extensive exposure mapping in urban areas through deep analysis of street-level pictures for floor count determination. Urban Science, 1(2), 16.
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Ilic, L., Sawada, M., & Zarzelli, A. (2019). Deep mapping gentrification in a large canadian city using deep learning and google street view. PloS One, 14(3), e0212814-e0212814. doi:10.1371/journal.pone.0212814
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Jiang, B. (2014). Establishing dose-response curves for the impact of urban forests on recovery from acute stress and landscape preference. Thesis
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Jiang, Z., Chen, L., Zhou, B., Huang, J., Xie, T., Fan, X., & Wang, C. (2020). iTV: Inferring traffic violation-prone locations with vehicle trajectories and road environment data. IEEE Systems Journal, , 1-12. doi:10.1109/JSYST.2020.3012743
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Kang, J., Körner, M., Wang, Y., Taubenböck, H., & Zhu, X. X. (2018). Building instance classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 44-59. doi:10.1016/j.isprsjprs.2018.02.006
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Kauer, T., Joglekar, S., Redi, M., Aiello, L. M., & Quercia, D. (2018). Mapping and visualizing deep-learning urban beautification. IEEE Computer Graphics and Applications, 38(5), 70-83. doi:10.1109/MCG.2018.053491732
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Kelly, C. M., Wilson, J. S., Baker, E. A., Miller, D. K., & Schootman, M. (2013). Using Google Street View to audit the built environment: inter-rater reliability results. Annals of Behavioral Medicine, 45(suppl_1), S108-S112.
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Kepper, M. M., PhD, Sothern, M. S., PhD, Theall, K. P., PhD, Griffiths, L. A., MPH, Scribner, Richard A., MD, MPH, Tseng, T., PhD, . . . Broyles, S. T., PhD. (2016;). A reliable, feasible method to observe neighborhoods at high spatial resolution. American Journal of Preventive Medicine, 52(1), S20-S30. doi:10.1016/j.amepre.2016.06.010
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Keralis, J. M., Javanmardi, M., Khanna, S., Dwivedi, P., Huang, D., Tasdizen, T., & Nguyen, Q. C. (2020). Health and the built environment in united states cities: Measuring associations using google street view-derived indicators of the built environment. BMC Public Health, 20(1), 215-215. doi:10.1186/s12889-020-8300-1
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Kim, E. J., Won, J., & Kim, J. (2019). Is Seoul walkable? Assessing a walkability score and examining its relationship with pedestrian satisfaction in Seoul, Korea. Sustainability, 11, 6915.
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Kim, H., Kang, Y., & Han, S. (2014). Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System. Hindawi, 1-11. doi:10.1155/2014/383270
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Kita, K., & Kidziński, Ł. (2019). Google street view image of a house predicts car accident risk of its resident.
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Kronkvist (2013). Systematic social observation of physical disorder in inner-city urban neighborhoods through Google Street View. Thesis
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Krylov, V. A., Kenny, E., & Dahyot, R. (2018). Automatic discovery and geotagging of objects from street view imagery. Remote Sensing, 10(5), 661.
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Kumakoshi, Y., Chan, S. Y., Koizumi, H., Li, X., & Yoshimura, Y. (2020). Standardized green view index and quantification of different metrics of urban green vegetation. Sustainability (Basel, Switzerland), 12(18), 7434. doi:10.3390/su12187434
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Kurka, J. M., Adams, M. A., Geremia, C., Zhu, W., Cain, K. L., Conway, T. L., & Sallis, J. F. (2016). Comparison of field and online observations for measuring land uses using the microscale audit of pedestrian streetscapes (MAPS). Journal of Transport & Health, 3(3), 278-286. https://doi.org/10.1016/j.jth.2016.05.001
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Langton, S. H., & Steenbeek, W. (2017). Residential burglary target selection: An analysis at the property-level using google street view. Applied Geography (Sevenoaks), 86, 292-299. doi:10.1016/j.apgeog.2017.06.014
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Larkin, A., & Hystad, P. (2019). Evaluating street view exposure measures of visible green space for health research. Journal of Exposure Science & Environmental Epidemiology, 29(4), 447-456. doi:10.1038/s41370-018-0017-1