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Decoding human safety perception with eye-tracking systems, street view images, and explainable AI

Datos Bibliográficos

ID7150554
AutoresYuhao Kang (0000-0003-3810-9450, Massachusetts Institute of Technology), Junda Chen (0000-0002-6649-2859, University of California San Diego), Liu (0000-0002-8106-2291), Liu Liu (0000-0002-3651-544X, Massachusetts Institute of Technology), Kshitij Sharma (0000-0003-3364-637X, Norwegian University of Science and Technology), Martina Mazzarello (0000-0001-8777-3927, Massachusetts Institute of Technology), Simone Mora (0000-0002-7991-1346, Norwegian University of Science and Technology), Fábio Duarte (0000-0003-0909-5379, Massachusetts Institute of Technology, autor de correspondencia), Carlo Ratti (0000-0002-1065-2972, Massachusetts Institute of Technology)
Año2026
Volumen123
Páginas102356
Fecha de publicación2026-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaComputers Environment and Urban Systems (JOURNAL)
Identificadores de la revistaISSN: 0198-9715 • E-ISSN: 1873-7587
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.compenvurbsys.2025.102356
OpenAlexW7103149149
IdiomaEN
Citas recibidas1
Referencias citadas52

Feeling · Perception · Public space · Signage · Visual perception · Evacuation and Crowd Dynamics · Human Factors and Ergonomics · Human-Automation Interaction and Safety · Safety Warnings and Signage

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  • A human-machine adversarial scoring framework for urban perception assessment using street-view images

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  • The global landscape of AI ethics guidelines

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  • Place identity

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  • “In the evening, I don’t walk in the park”

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  • Assessing impacts of objective features and subjective perceptions of street environment on running amount

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  • Assessing differences in safety perceptions using GeoAI and survey across neighbourhoods in Stockholm, Sweden

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  • Predicting perceptions of the built environment using GIS, satellite and street view image approaches

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  • Street view imagery in urban analytics and GIS

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  • Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning

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  • Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents’ mental health

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  • What Makes a Place Safe? Assessing AI-Generated Safety Perception Scores Using Stockholm’s Street View Images

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Obras citantes distintas1
Citas por año1
Intervalo de citas2026 - 2026 (1)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 1
Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae