Revealing spatio-temporal evolution of urban visual environments with street view imagery

[1]  A. Yeh,et al.  Mapping Seasonal Changes of Street Greenery Using Multi-Temporal Street-View Images , 2023, Sustainable Cities and Society.

[2]  K. Rahimi,et al.  An interpretable machine learning framework for measuring urban perceptions from panoramic street view images , 2023, iScience.

[3]  F. Biljecki,et al.  Sensing urban soundscapes from street view imagery , 2023, Comput. Environ. Urban Syst..

[4]  F. Biljecki,et al.  A comprehensive framework for evaluating the quality of street view imagery , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[5]  F. Biljecki,et al.  Water View Imagery: Perception and evaluation of urban waterscapes worldwide , 2022, Ecological Indicators.

[6]  Xinyu Chen,et al.  Mining real estate ads and property transactions for building and amenity data acquisition , 2022, Urban Informatics.

[7]  F. Biljecki,et al.  Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions , 2022, Environment and Planning B: Urban Analytics and City Science.

[8]  H. Ledoux,et al.  Reconstructing historical 3D city models , 2022, Urban informatics.

[9]  Nicole C. Inglis,et al.  From viewsheds to viewscapes: Trends in landscape visibility and visual quality research , 2022, Landscape and Urban Planning.

[10]  Andrew Y. Ng,et al.  Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery , 2022, Environment and Planning B: Urban Analytics and City Science.

[11]  F. Biljecki,et al.  Global Building Morphology Indicators , 2022, Comput. Environ. Urban Syst..

[12]  Y. Her,et al.  Spatio-temporal monitoring of urban street-side vegetation greenery using Baidu Street View images , 2022, Urban Forestry & Urban Greening.

[13]  Youngchul Kim,et al.  A street-view-based method to detect urban growth and decline: A case study of Midtown in Detroit, Michigan, USA , 2022, PloS one.

[14]  Abraham Noah Wu,et al.  GANmapper: geographical data translation , 2021, Int. J. Geogr. Inf. Sci..

[15]  F. Biljecki,et al.  Street view imagery in urban analytics and GIS: A review , 2021 .

[16]  Jiangping Zhou,et al.  Revitalizing historic districts: Identifying built environment predictors for street vibrancy based on urban sensor data , 2021 .

[17]  Geonhwa You,et al.  Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big Data , 2021, Annals of the American Association of Geographers.

[18]  Qingfeng Guan,et al.  Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique , 2021, City and Environment Interactions.

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

[20]  Filip Biljecki,et al.  Assessing bikeability with street view imagery and computer vision , 2021, Transportation Research Part C: Emerging Technologies.

[21]  Zhongwei Shen,et al.  Measuring the built environment of green transit-oriented development: A factor-cluster analysis of rail station areas in Singapore , 2021 .

[22]  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.

[23]  Lishuai Li,et al.  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 , 2021, Landscape and Urban Planning.

[24]  Filip Biljecki,et al.  3D city models for urban farming site identification in buildings , 2020, Comput. Environ. Urban Syst..

[25]  Abraham Noah Wu,et al.  Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability , 2020, ArXiv.

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

[27]  Daniel Arribas-Bel,et al.  Using convolutional autoencoders to extract visual features of leisure and retail environments , 2020 .

[28]  Tomoki Nakaya,et al.  Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of Google Street View images. , 2020, Health & place.

[29]  Jibo He,et al.  Perceived Green at Speed: A Simulated Driving Experiment Raises New Questions for Attention Restoration Theory and Stress Reduction Theory , 2020 .

[30]  Warren C. Jochem,et al.  Classifying settlement types from multi-scale spatial patterns of building footprints , 2020, Environment and Planning B: Urban Analytics and City Science.

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

[32]  Devis Tuia,et al.  Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data , 2018, Int. J. Geogr. Inf. Sci..

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

[34]  Ying Long,et al.  Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing , 2019, Landscape and Urban Planning.

[35]  Carlo Ratti,et al.  Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model , 2019, Landscape and Urban Planning.

[36]  M. Berghauser Pont,et al.  Towards analytical typologies of plot systems: Quantitative profile of five European cities , 2019, Environment and Planning B: Urban Analytics and City Science.

[37]  Hao Zhou,et al.  Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning , 2019, Sustainable Cities and Society.

[38]  Chenghu Zhou,et al.  Sensing multiple semantics of urban space from crowdsourcing positioning data , 2019, Cities.

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

[40]  Huiping Liu,et al.  Spatiotemporal evolution analysis of time-series land use change using self-organizing map to examine the zoning and scale effects , 2019, Comput. Environ. Urban Syst..

[41]  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.

[42]  Yao Yao,et al.  Hierarchical community detection and functional area identification with OSM roads and complex graph theory , 2019, Int. J. Geogr. Inf. Sci..

[43]  Li Zhuo,et al.  Re-examining urban region and inferring regional function based on spatial–temporal interaction , 2019, Int. J. Digit. Earth.

[44]  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.

[45]  Sven Casteleyn,et al.  The Lisbon ranking for smart sustainable cities in Europe , 2019, Sustainable Cities and Society.

[46]  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.

[47]  Ricardo Hurtubia,et al.  Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach , 2019, Landscape and Urban Planning.

[48]  Bolei Zhou,et al.  Measuring human perceptions of a large-scale urban region using machine learning , 2018, Landscape and Urban Planning.

[49]  Paolo Santi,et al.  Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data , 2018, Trans. GIS.

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

[51]  Yichun Xie,et al.  A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit , 2018 .

[52]  Wei Tu,et al.  Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data , 2018, Remote. Sens..

[53]  Hui Wang,et al.  A machine learning-based method for the large-scale evaluation of the qualities of the urban environment , 2017, Comput. Environ. Urban Syst..

[54]  Ramesh Raskar,et al.  Computer vision uncovers predictors of physical urban change , 2017, Proceedings of the National Academy of Sciences.

[55]  Krzysztof Janowicz,et al.  Extracting urban functional regions from points of interest and human activities on location‐based social networks , 2017, Trans. GIS.

[56]  P. Corona,et al.  A comprehensive insight into the geography of forest cover in Italy: Exploring the importance of socioeconomic local contexts , 2017 .

[57]  Omid Kardan,et al.  The order of disorder: Deconstructing visual disorder and its effect on rule-breaking. , 2016, Journal of experimental psychology. General.

[58]  Alexander Zipf,et al.  Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks , 2016, Int. J. Geogr. Inf. Sci..

[59]  Ramesh Raskar,et al.  Deep Learning the City: Quantifying Urban Perception at a Global Scale , 2016, ECCV.

[60]  William C. Sullivan,et al.  A Dose-Response Curve Describing the Relationship Between Urban Tree Cover Density and Self-Reported Stress Recovery , 2016 .

[61]  Alexander Zipf,et al.  Identifying the city center using human travel flows generated from location-based social networking data , 2016 .

[62]  M. Grossberg,et al.  A global empirical typology of anthropogenic drivers of environmental change in deltas , 2016, Sustainability Science.

[63]  Bernard De Baets,et al.  The acoustic summary as a tool for representing urban sound environments , 2015 .

[64]  Michael Luca,et al.  Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life , 2015 .

[65]  Austin Troy,et al.  Effects of skeletal streetscape design on perceived safety , 2015 .

[66]  E. Bergmeier,et al.  Plant species richness patterns along a gradient of landscape modification intensity in Lower Saxony, Germany , 2015 .

[67]  S. Spielman,et al.  Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach , 2015 .

[68]  Dongmei Chen,et al.  An Unsupervised Urban Change Detection Procedure by Using Luminance and Saturation for Multispectral Remotely Sensed Images , 2015 .

[69]  Weidong Li,et al.  Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset , 2015, ISPRS Int. J. Geo Inf..

[70]  B. Deal,et al.  A dose-response curve describing the relationship between tree cover density and landscape preference , 2015 .

[71]  Wei Song,et al.  Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China , 2014, Int. J. Geogr. Inf. Sci..

[72]  Bolei Zhou,et al.  Recognizing City Identity via Attribute Analysis of Geo-tagged Images , 2014, ECCV.

[73]  Å. Sang,et al.  Landscape assessment in metropolitan areas – developing a visual indicator-based approach , 2014 .

[74]  Henriette Cramer,et al.  Aesthetic capital: what makes london look beautiful, quiet, and happy? , 2014, CSCW.

[75]  César A. Hidalgo,et al.  The Collaborative Image of The City: Mapping the Inequality of Urban Perception , 2013, PloS one.

[76]  Alexei A. Efros,et al.  What makes Paris look like Paris? , 2015, Commun. ACM.

[77]  L. Bettencourt,et al.  A unified theory of urban living , 2010, Nature.

[78]  P. Newman Green Urbanism and its Application to Singapore , 2010 .

[79]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[80]  Reid Ewing,et al.  Creating and validating GIS measures of urban design for health research. , 2009, Journal of environmental psychology.

[81]  J. Qi,et al.  Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization , 2009 .

[82]  Susan L Handy,et al.  Measuring the Unmeasurable: Urban Design Qualities Related to Walkability , 2009 .

[83]  G. Fry,et al.  Relationships between visual landscape preferences and map-based indicators of landscape structure , 2006 .

[84]  Belinda Yuen,et al.  Reclaiming Cultural Heritage in Singapore , 2006 .

[85]  Åsa Ode,et al.  Key concepts in a framework for analysing visual landscape character , 2006 .

[86]  S. Porta,et al.  Linking urban design to sustainability: formal indicators of social urban sustainability field research in Perth, Western Australia , 2005 .

[87]  L. Jackson The relationship of urban design to human health and condition , 2003 .

[88]  Anna Jorgensen,et al.  Woodland spaces and edges: their impact on perception of safety and preference , 2002 .

[89]  Simon Bell,et al.  Landscape pattern, perception and visualisation in the visual management of forests , 2001 .

[90]  T. Daniel Whither scenic beauty? Visual landscape quality assessment in the 21st century , 2001 .

[91]  Christian L Krause,et al.  Our visual landscape: Managing the landscape under special consideration of visual aspects , 2001 .

[92]  A. Lothian Landscape and the philosophy of aesthetics: is landscape quality inherent in the landscape or in the eye of the beholder? , 1999 .

[93]  Stephen Kaplan,et al.  The restorative benefits of nature: Toward an integrative framework , 1995 .

[94]  Terry J. Brown,et al.  Environmental Preference , 1989 .

[95]  Richard Smardon,et al.  Perception and aesthetics of the urban environment: Review of the role of vegetation , 1988 .

[96]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[97]  R. Kaplan,et al.  Cultural and sub-cultural comparisons in preferences for natural settings , 1987 .

[98]  T. Daniel,et al.  Methodological Issues in the Assessment of Landscape Quality , 1983 .

[99]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .