Towards Human-centric Digital Twins: Leveraging Computer Vision and Graph Models to Predict Outdoor Comfort

[1]  J. Stoter,et al.  Challenges of urban digital twins: A systematic review and a Delphi expert survey , 2023, Automation in Construction.

[2]  R. Stouffs,et al.  Assessing and benchmarking 3D city models , 2022, Int. J. Geogr. Inf. Sci..

[3]  Jiaxin Du,et al.  Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda , 2022, Journal of planning literature.

[4]  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.

[5]  Jia Siqi,et al.  Influences of the thermal environment on pedestrians’ thermal perception and travel behavior in hot weather , 2022, Building and Environment.

[6]  Mark Stevenson,et al.  Isolating the impacts of urban form and fabric from geography on urban heat and human thermal comfort , 2022, Building and Environment.

[7]  P. Liu,et al.  Coupling a Physical Replica with a Digital Twin: A Comparison of Participatory Decision-Making Methods in an Urban Park Environment , 2022, ISPRS Int. J. Geo Inf..

[8]  I. Pigliautile,et al.  Multi-domain human-oriented approach to evaluate human comfort in outdoor environments , 2022, International Journal of Biometeorology.

[9]  Wenxiu Gao,et al.  Assessment of sidewalk walkability: Integrating objective and subjective measures of identical context-based sidewalk features , 2022, Sustainable Cities and Society.

[10]  Abraham Noah Wu,et al.  Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales , 2022, Building and Environment.

[11]  F. Biljecki,et al.  A review of spatially-explicit GeoAI applications in Urban Geography , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[12]  T. Fukuda,et al.  Measuring Visual Walkability Perception Using Panoramic Street View Images, Virtual Reality, and Deep Learning , 2022, SSRN Electronic Journal.

[13]  Linghan Shan,et al.  The superposition effects of air pollution on government health expenditure in China— spatial evidence from GeoDetector , 2022, BMC Public Health.

[14]  T. Toivonen,et al.  Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model , 2022, Journal of Spatial Information Science.

[15]  K. Steemers,et al.  Urban climate walk: A stop-and-go assessment of the dynamic thermal sensation and perception in two waterfront districts in Rome, Italy , 2022, Building and Environment.

[16]  Peixian Li,et al.  Non-intrusive comfort sensing: Detecting age and gender from infrared images for personal thermal comfort , 2022, Building and Environment.

[17]  Rui Cao,et al.  Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction , 2022, Comput. Environ. Urban Syst..

[18]  Donghyun Kim,et al.  A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction , 2022, Sustainability.

[19]  Z. Fang,et al.  Modelling people’s perceived scene complexity of real-world environments using street-view panoramas and open geodata , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.

[20]  C. Ye,et al.  Combining spatial response features and machine learning classifiers for landslide susceptibility mapping , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Clayton Miller,et al.  Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models , 2022, Building and Environment.

[22]  N. Mostofi,et al.  Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses , 2022, Papers in Regional Science.

[23]  Marianna Charitonidou Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making , 2022, International Journal of Architectural Computing.

[24]  Ha-Yeong Yoon,et al.  Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving , 2022, Sensors.

[25]  Jianzhe Lin,et al.  CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials , 2022, Sustainable Cities and Society.

[26]  K. Janowicz,et al.  A review of location encoding for GeoAI: methods and applications , 2021, Int. J. Geogr. Inf. Sci..

[27]  Sven Gowal,et al.  Data Augmentation Can Improve Robustness , 2021, NeurIPS.

[28]  Yu Liu,et al.  Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions , 2021, GeoInformatica.

[29]  Longfeng Wu,et al.  Predicting the effect of street environment on residents' mood states in large urban areas using machine learning and street view images , 2021, Science of The Total Environment.

[30]  Clayton Miller,et al.  Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec , 2021, Building and Environment.

[31]  C. Ataman,et al.  Urban Interventions and Participation Tools in Urban Design Processes: A Systematic Review and Thematic Analysis (1995 – 2021) , 2021, Sustainable Cities and Society.

[32]  Zhanghua Wu,et al.  Street-level solar radiation mapping and patterns profiling using Baidu Street View images , 2021 .

[33]  Huidong Li,et al.  Urban morphology in China: Dataset development and spatial pattern characterization , 2021 .

[34]  Guangwen Song,et al.  Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery , 2021, Comput. Environ. Urban Syst..

[35]  Taghi M. Khoshgoftaar,et al.  Text Data Augmentation for Deep Learning , 2021, Journal of Big Data.

[36]  Yongqiang Cheng,et al.  A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception , 2021, ISPRS Int. J. Geo Inf..

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

[38]  Giuseppe Peronato,et al.  Designing and assessing solar energy neighborhoods from visual impact , 2021 .

[39]  Shao Zhenfeng,et al.  Smart city based on digital twins , 2021, Computational Urban Science.

[40]  Irem Y. Tumer,et al.  Digital Twin-Driven Human-Centered Design Frameworks for Meeting Sustainability Objectives , 2021 .

[41]  Hugo Wai Leung Mak,et al.  Comparative assessments and insights of data openness of 50 smart cities in air quality aspects , 2021 .

[42]  Nuria Oliver,et al.  Ethical machines: The human-centric use of artificial intelligence , 2021, iScience.

[43]  Pengyuan Liu,et al.  A graph-based semi-supervised approach to classification learning in digital geographies , 2021, Comput. Environ. Urban Syst..

[44]  Xinyue Ye,et al.  Sidewalk extraction using aerial and street view images , 2021, Environment and Planning B: Urban Analytics and City Science.

[45]  N. Biloria,et al.  Outdoor thermal comfort: Analyzing the impact of urban configurations on the thermal performance of street canyons in the humid subtropical climate of Sydney , 2020, Frontiers of Architectural Research.

[46]  Carlo Ratti,et al.  Desirable streets: Using deviations in pedestrian trajectories to measure the value of the built environment , 2021, Comput. Environ. Urban Syst..

[47]  N. Nazarian,et al.  Personal assessment of urban heat exposure: a systematic review , 2020 .

[48]  Krithi Ramamritham,et al.  Predicting human perception of the urban environment in a spatiotemporal urban setting using locally acquired street view images and audio clips , 2020 .

[49]  A. Zaballos,et al.  A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring , 2020, Sustainability.

[50]  J. Jeon,et al.  The influence of human behavioral characteristics on soundscape perception in urban parks: Subjective and observational approaches , 2020 .

[51]  Chao Sun,et al.  Evaluation of vehicle vibration comfort using deep learning , 2020 .

[52]  Cho Kwong Charlie Lam,et al.  Cross-modal effects of thermal and visual conditions on outdoor thermal and visual comfort perception , 2020 .

[53]  Carlo Ratti,et al.  Air quality monitoring using mobile low-cost sensors mounted on trash-trucks: Methods development and lessons learned , 2020 .

[54]  C. Gargiulo,et al.  Defining the characteristics of walking paths to promote an active ageing , 2020 .

[55]  Li Yang,et al.  On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice , 2020, Neurocomputing.

[56]  Wenwen Li,et al.  GeoAI: Where machine learning and big data converge in GIScience , 2020, J. Spatial Inf. Sci..

[57]  Yee Whye Teh,et al.  AI for social good: unlocking the opportunity for positive impact , 2020, Nature Communications.

[58]  Jimin Wang,et al.  NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages , 2020, Trans. GIS.

[59]  Yu Liu,et al.  Understanding Place Characteristics in Geographic Contexts through Graph Convolutional Neural Networks , 2020, Smart Spaces and Places.

[60]  Herman van der Auweraer,et al.  Digital Twins , 2020, SEMA SIMAI Springer Series.

[61]  Dengyin Zhang,et al.  Iterative Reweighted Tikhonov-Regularized Multihypothesis Prediction Scheme for Distributed Compressive Video Sensing , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[62]  Dengyin Zhang,et al.  JsrNet: A Joint Sampling–Reconstruction Framework for Distributed Compressive Video Sensing , 2019, Sensors.

[63]  Krzysztof Janowicz,et al.  GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond , 2019, Int. J. Geogr. Inf. Sci..

[64]  Yongping Zhang,et al.  Spatial pattern of leisure activities among residents in Beijing, China: Exploring the impacts of urban environment , 2020 .

[65]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[66]  Marialena Nikolopoulou,et al.  Outdoor thermal comfort for pedestrians in movement: thermal walks in complex urban morphology , 2019, International Journal of Biometeorology.

[67]  S. Nahavandi Industry 5.0—A Human-Centric Solution , 2019, Sustainability.

[68]  Yvan Bédard,et al.  SPATIAL DATA UNCERTAINTY IN THE VGI WORLD: GOING FROM CONSUMER TO PRODUCER , 2019 .

[69]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[70]  C. Jacobs,et al.  Patterns of outdoor exposure to heat in three South Asian cities. , 2019, The Science of the total environment.

[71]  Zohair Al-Ameen,et al.  Nighttime image enhancement using a new illumination boost algorithm , 2019, IET Image Process..

[72]  Yunwoo Nam,et al.  The influence of built environment features on crowdsourced physiological responses of pedestrians in neighborhoods , 2019, Comput. Environ. Urban Syst..

[73]  Daniel Arribas-Bel,et al.  Understanding the dynamics of urban areas of interest through volunteered geographic information , 2018, J. Geogr. Syst..

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

[75]  Haifeng Li,et al.  Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method , 2018, ArXiv.

[76]  Dengyin Zhang,et al.  A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior , 2018, Mathematical Problems in Engineering.

[77]  Joyce Kim,et al.  Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control , 2018 .

[78]  Can Chen,et al.  Perceptual hash algorithm-based adaptive GOP selection algorithm for distributed compressive video sensing , 2018, IET Image Process..

[79]  Liu Feng,et al.  City Brain, a New Architecture of Smart City Based on the Internet Brain , 2017, 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD)).

[80]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[81]  David Montes González,et al.  Relationships among satisfaction, noise perception, and use of urban green spaces. , 2018, The Science of the total environment.

[82]  H. Hong,et al.  Deep learning architectures for multi-label classification of intelligent health risk prediction , 2017, BMC Bioinformatics.

[83]  Carol M. Werner,et al.  Analyzing walking route choice through built environments using random forests and discrete choice techniques , 2017, Environment and planning. B, urban analytics and city science.

[84]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[85]  Qi Meng,et al.  Effect of sound-related activities on human behaviours and acoustic comfort in urban open spaces. , 2016, The Science of the total environment.

[86]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[87]  Asya Natapov,et al.  Visibility of urban activities and pedestrian routes: An experiment in a virtual environment , 2016, Comput. Environ. Urban Syst..

[88]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[89]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  Filip Biljecki,et al.  Applications of 3D City Models: State of the Art Review , 2015, ISPRS Int. J. Geo Inf..

[91]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[92]  Shinichi Kitamura,et al.  Investigation of Factors Affecting the Evaluation of Streetscapes in Japan and China , 2013 .

[93]  Weidong Song,et al.  The role of mobile volunteered geographic information in urban management , 2010, 2010 18th International Conference on Geoinformatics.

[94]  Ria Hutabarat Lo Walkability: what is it? , 2009 .

[95]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

[96]  Mario Schootman,et al.  The association of sidewalk walkability and physical disorder with area-level race and poverty , 2007, Journal of Epidemiology & Community Health.

[97]  R. Rhodes,et al.  Relationship between regular walking, physical activity, and health-related quality of life. , 2007, Journal of physical activity & health.

[98]  P. Patterson,et al.  Urban Form and Older Residents' Service Use, Walking, Driving, Quality of Life, and Neighborhood Satisfaction , 2004, American journal of health promotion : AJHP.

[99]  Xiao-Li Meng,et al.  The Art of Data Augmentation , 2001 .

[100]  André Potvin,et al.  Assessing the Microclimate of Urban Transitional Spaces , 2000 .

[101]  Tawfiq M. Abu-Ghazzeh Communicating Behavioral Research to Campus Design , 1999 .

[102]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[103]  Bradley Efron,et al.  Missing Data, Imputation, and the Bootstrap , 1994 .

[104]  L. M. Anderson,et al.  Perception of Personal Safety in Urban Recreation Sites , 1984 .

[105]  G. Jenks The Data Model Concept in Statistical Mapping , 1967 .