A review on street view observations in support of the sustainable development goals

[1]  Hanlin Zhou,et al.  Impacts of perceived safety and beauty of park environments on time spent in parks: Examining the potential of street view imagery and phone-based GPS data , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Le Wang,et al.  Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[3]  H. Miller,et al.  How does street space influence crash frequency? An analysis using segmented street view imagery , 2022, Environment and Planning B: Urban Analytics and City Science.

[4]  H. Miller,et al.  Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data. , 2022, Health & place.

[5]  Linchuan Yang,et al.  Sustainable built environment for facilitating public health of older adults: Evidence from Hong Kong , 2022, Sustainable Development.

[6]  T. Tasdizen,et al.  Google Street View Images as Predictors of Patient Health Outcomes, 2017–2019 , 2022, Big Data Cogn. Comput..

[7]  P. Hystad,et al.  Predicting Perceptions of the Built Environment using GIS, Satellite and Street View Image Approaches. , 2021, Landscape and urban planning.

[8]  B. Loo,et al.  Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science , 2021, Computational Urban Science.

[9]  C. Ratti,et al.  Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling , 2021 .

[10]  J. Pearce,et al.  Dynamic greenspace exposure and residents’ mental health in Guangzhou, China: From over-head to eye-level perspective, from quantity to quality , 2021 .

[11]  C. Ren,et al.  Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong , 2021 .

[12]  Xiaojiang Li,et al.  Exposure to urban green space may both promote and harm mental health in socially vulnerable neighborhoods: A neighborhood-scale analysis in New York City. , 2021, Environmental research.

[13]  T. Tasdizen,et al.  Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes , 2021, International journal of environmental research and public health.

[14]  Jing-zhu Zhao,et al.  Quantifying physical and psychological perceptions of urban scenes using deep learning , 2021, Land Use Policy.

[15]  J. Pearce,et al.  Are greenspace quantity and quality associated with mental health through different mechanisms in Guangzhou, China: A comparison study using street view data. , 2021, Environmental pollution.

[16]  Qingfeng Guan,et al.  Discovering the homogeneous geographic domain of human perceptions from street view images , 2021 .

[17]  L. Knibbs,et al.  Street view greenness is associated with lower risk of obesity in adults: Findings from the 33 Chinese community health study. , 2021, Environmental research.

[18]  S. Guhathakurta,et al.  How are Neighborhood and Street-Level Walkability Factors Associated with Walking Behaviors? A Big Data Approach Using Street View Images , 2021, Environment and Behavior.

[19]  Yao Yao,et al.  Perceived influence of street-level visible greenness exposure in the work and residential environment on life satisfaction: Evidence from Beijing, China , 2021, Urban Forestry & Urban Greening.

[20]  G. Alpert,et al.  Automatic large scale detection of red palm weevil infestation using street view images , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[21]  Guanghao Li,et al.  Urban neighbourhood environment assessment based on street view image processing: A review of research trends , 2021 .

[22]  C. Ratti,et al.  “Perception bias”: Deciphering a mismatch between urban crime and perception of safety , 2021, Landscape and Urban Planning.

[23]  S. Hankey,et al.  Using Street View Imagery to Predict Street-Level Particulate Air Pollution. , 2021, Environmental science & technology.

[24]  Stella X. Yu,et al.  Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management , 2021, Automation in Construction.

[25]  Suhong Zhou,et al.  Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China , 2021, International journal of environmental research and public health.

[26]  J. She,et al.  Evaluating Street Greenery by Multiple Indicators Using Street-Level Imagery and Satellite Images: A Case Study in Nanjing, China , 2020, Forests.

[27]  E. Steiner,et al.  Ejidos, Urbanization, and the Production of Inequality in Formerly Agricultural Lands, Guadalajara, Mexico, 1975–2020 , 2020, Land.

[28]  J. Cyrys,et al.  Spatiotemporal Characteristics and Driving Factors of Black Carbon in Augsburg, Germany: Combination of Mobile Monitoring and Street View Images. , 2020, Environmental science & technology.

[29]  P. Jia.  Obesogenic environment and childhood obesity , 2020, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[30]  Tolga Tasdizen,et al.  Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes , 2020, Public health reports.

[31]  Stephen J. Mooney,et al.  Using Universal Kriging to Improve Neighborhood Physical Disorder Measurement , 2020, Sociological methods & research.

[32]  Yuan Meng,et al.  Sensing urban poverty: From the perspective of human perception-based greenery and open-space landscapes , 2020, Comput. Environ. Urban Syst..

[33]  Sheng Li,et al.  Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China , 2020, Comput. Environ. Urban Syst..

[34]  Andrew Butt,et al.  A human-centred assessment framework to prioritise heat mitigation efforts for active travel at city scale. , 2020, The Science of the total environment.

[35]  S. Lindley,et al.  Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions , 2020, Science of The Total Environment.

[36]  Zhongwen Luo,et al.  Synthesizing location semantics from street view images to improve urban land-use classification , 2020, Int. J. Geogr. Inf. Sci..

[37]  Yu Liu,et al.  An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data , 2020, Remote. Sens..

[38]  Zheng Liu,et al.  Multi-source urban data fusion for property value assessment: A case study in Philadelphia , 2020, Neurocomputing.

[39]  Tolga Tasdizen,et al.  Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases , 2020, International journal of environmental research and public health.

[40]  Jianming Liang,et al.  Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data , 2020, ISPRS Int. J. Geo Inf..

[41]  Zongming Wang,et al.  Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data , 2020, Remote. Sens..

[42]  Yi Lu,et al.  Association of street greenery and physical activity in older adults: A novel study using pedestrian-centered photographs , 2020 .

[43]  Hui Lin,et al.  A review of urban physical environment sensing using street view imagery in public health studies , 2020, Ann. GIS.

[44]  Alejandro Betancourt,et al.  Automatic detection of building typology using deep learning methods on street level images , 2020 .

[45]  T. Tasdizen,et al.  Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States , 2020, International journal of environmental research and public health.

[46]  J. Ning,et al.  How long is the sun duration in a street canyon? —— Analysis of the view factors of street canyons , 2020 .

[47]  Yao Yao,et al.  Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China. , 2020, The Science of the total environment.

[48]  Devin K. Harris,et al.  A big data analytics strategy for scalable urban infrastructure condition assessment using semi-supervised multi-transform self-training , 2020 .

[49]  K. Wen,et al.  The Impact of Street Space Perception Factors on Elderly Health in High-Density Cities in Macau—Analysis Based on Street View Images and Deep Learning Technology , 2020, Sustainability.

[50]  Fan Yang,et al.  Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas , 2020, Int. J. Digit. Earth.

[51]  Quynh C. Nguyen,et al.  Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment , 2020, BMC Public Health.

[52]  Liu Wei,et al.  Urban Function as a New Perspective for Adaptive Street Quality Assessment , 2020, Sustainability.

[53]  Wei Chen,et al.  Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data - A Case Study of Shanghai, China , 2020, ISPRS Int. J. Geo Inf..

[54]  Yimeng Song,et al.  Natural outdoor environment, neighbourhood social cohesion and mental health: Using multilevel structural equation modelling, streetscape and remote-sensing metrics , 2020 .

[55]  Gaoyuan Yang,et al.  How can urban blue-green space be planned for climate adaption in high-latitude cities? A seasonal perspective , 2020 .

[56]  Iacopo Bernetti,et al.  Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services , 2020, Remote. Sens..

[57]  Jianguo Liu,et al.  Assessing progress towards sustainable development over space and time , 2020, Nature.

[58]  Blair T. Johnson,et al.  Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement. , 2020, Health & place.

[59]  Qingyan Meng,et al.  Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index , 2019 .

[60]  Zhaoya Gong,et al.  Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions , 2019, Sustainability.

[61]  George Grekousis,et al.  Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique , 2019, Comput. Environ. Urban Syst..

[62]  Ruoyu Wang,et al.  Association between street greenery and walking behavior in older adults in Hong Kong , 2019, Sustainable Cities and Society.

[63]  Yi Lu,et al.  Using Google Street View to investigate the association between street greenery and physical activity , 2019, Landscape and Urban Planning.

[64]  Yi Lu,et al.  Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices , 2019, Landscape and Urban Planning.

[65]  Jon E. Froehlich,et al.  Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery , 2019, ASSETS.

[66]  M. Stevenson,et al.  Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning , 2019, Urban Climate.

[67]  Qian Sun,et al.  Detecting and mapping traffic signs from Google Street View images using deep learning and GIS , 2019, Comput. Environ. Urban Syst..

[68]  Yao Yao,et al.  Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents' mental health. , 2019, Health & place.

[69]  Yao Yao,et al.  The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques , 2019, International Journal of Health Geographics.

[70]  Jiale Wang,et al.  A human-machine adversarial scoring framework for urban perception assessment using street-view images , 2019, Int. J. Geogr. Inf. Sci..

[71]  Lun Wu,et al.  Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[72]  Yao Yao,et al.  The relationship between visual enclosure for neighbourhood street walkability and elders’ mental health in China: Using street view images , 2019, Journal of Transport & Health.

[73]  Perry Hystad,et al.  Evaluating street view exposure measures of visible green space for health research , 2019, Journal of Exposure Science & Environmental Epidemiology.

[74]  Xiao Fu,et al.  Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning , 2019, PloS one.

[75]  Xiao Xiang Zhu,et al.  Model Fusion for Building Type Classification from Aerial and Street View Images , 2019, Remote. Sens..

[76]  Mark Stevenson,et al.  Streetscape augmentation using generative adversarial networks: insights related to health and wellbeing , 2019, Sustainable Cities and Society.

[77]  Yao Yao,et al.  Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures. , 2019, Environmental research.

[78]  Yu Ye,et al.  A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis , 2019, International journal of environmental research and public health.

[79]  Benjamin Bechtel,et al.  Urban climate zone classification using convolutional neural network and ground-level images , 2019, Progress in Physical Geography: Earth and Environment.

[80]  A. Stein,et al.  Earth Observation: Investigating Noncommunicable Diseases from Space. , 2019, Annual review of public health.

[81]  B. Turner,et al.  Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression , 2019, Landscape Ecology.

[82]  Kara E MacLeod,et al.  Pathways from built environment to health: A conceptual framework linking behavior and exposure-based impacts , 2019, Journal of Transport & Health.

[83]  Alfred Stein,et al.  Spatial Technologies in Obesity Research: Current Applications and Future Promise , 2019, Trends in Endocrinology & Metabolism.

[84]  Martha Bohm,et al.  Characterization of food cultivation along roadside transects with Google Street View imagery and deep learning , 2019, Comput. Electron. Agric..

[85]  Ross Maciejewski,et al.  Urban form and composition of street canyons: A human-centric big data and deep learning approach , 2019, Landscape and Urban Planning.

[86]  Roberto Marcondes Cesar Junior,et al.  Quantifying the Presence of Graffiti in Urban Environments , 2019, 2019 IEEE International Conference on Big Data and Smart Computing (BigComp).

[87]  L. Norford,et al.  Spatiotemporal patterns of street-level solar radiation estimated using Google Street View in a high-density urban environment , 2019, Building and Environment.

[88]  Yao Shen,et al.  Street-Frontage-Net: urban image classification using deep convolutional neural networks , 2018, Int. J. Geogr. Inf. Sci..

[89]  Wei Tu,et al.  Integrating Aerial and Street View Images for Urban Land Use Classification , 2018, Remote. Sens..

[90]  Alexander Binder,et al.  Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data , 2018, ECCV.

[91]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[92]  Kerrie L. Mengersen,et al.  Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review , 2018, Remote. Sens..

[93]  Christos Diou,et al.  Image-Based Surrogates of Socio-Economic Status in Urban Neighborhoods Using Deep Multiple Instance Learning , 2018, J. Imaging.

[94]  Wei Sun,et al.  Methods and datasets on semantic segmentation: A review , 2018, Neurocomputing.

[95]  Brooks Paige,et al.  Take a Look Around , 2018, ACM Trans. Intell. Syst. Technol..

[96]  Weidong Li,et al.  Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images , 2018, Sensors.

[97]  Edward Ng,et al.  Mapping sky, tree, and building view factors of street canyons in a high-density urban environment , 2018 .

[98]  Rencai Dong,et al.  Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing , 2018, ISPRS Int. J. Geo Inf..

[99]  Xiao Xiang Zhu,et al.  Building Instance Classification Using Street View Images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[100]  Ming Wen,et al.  Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research , 2018, Journal of Epidemiology & Community Health.

[101]  Denisa Rodila,et al.  Live Monitoring of Earth Surface (LiMES): A framework for monitoring environmental changes from Earth Observations , 2017 .

[102]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[103]  Alfred Stein,et al.  Using remote sensing technology to measure environmental determinants of non-communicable diseases. , 2017, International journal of epidemiology.

[104]  Yi Li,et al.  Automatic Sky View Factor Estimation from Street View Photographs - A Big Data Approach , 2017, Remote. Sens..

[105]  Jonathan Krause,et al.  Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States , 2017, Proceedings of the National Academy of Sciences.

[106]  Pietro Perona,et al.  Cataloging Public Objects Using Aerial and Street-Level Images — Urban Trees , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[107]  J. Ioannidis,et al.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration , 2009, BMJ : British Medical Journal.

[108]  Yimeng Song,et al.  Urban greenery mitigates the negative effect of urban density on older adults' life satisfaction: Evidence from Shanghai, China , 2022, Cities.

[109]  Youngryel Ryu,et al.  Exploring Google Street View with deep learning for crop type mapping , 2021 .

[110]  Sugie Lee,et al.  Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning , 2021 .

[111]  De Wang,et al.  Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data , 2019, Sustainability.

[112]  Julien O. Teitler,et al.  Using Google Street View to audit neighborhood environments. , 2011, American journal of preventive medicine.