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Deep learning meets urban design

Assessing streetscape aesthetic and design quality through AI and cluster analysis

Bibliographic Data

ID6419573
AuthorsHaoran Ma (0000-0003-1567-713X), Jie Li (0000-0002-5131-4769), Xinyue Ye (0000-0001-8838-9476)
Year2025
Volume162
Pages105939-105939
Publication date2025-04-04
Open AccessYes
TypeARTICLE
VenueCities (JOURNAL)
Journal identifiersISSN: 0264-2751 • E-ISSN: 1873-6084
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cities.2025.105939
OpenAlexW4409171747
LanguageNL
Citations received5
References cited48

Architectural engineering · Civil engineering · Cluster (spacecraft · Epistemology · Quality (philosophy · Urban design · Urban planning · Computer Science · Engineering · Noise Effects and Management · Philosophy · Psychology · Urban Design and Spatial Analysis · Urban Green Space and Health

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  • Same Streets, Different Contexts

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  • Seasonal Variations in the Restorative Quality Of Campus Landscapes

    Open Access•Haoran Ma, Mei-Po Kwan•Environment and Behavior•2026

  • Perceived distance to greenery affects psychological restoration

    Open Access•Haoran Ma, Yan Zhang et al.•Environment and Planning B Urban…•2026

  • Revealing emotional responses to urban environmental elements through street view data and deep learning

    Open Access•Li-Chih Ho, Y Wei et al.•Environment and Planning B Urban…•2026

  • Conflict, arousal, and curiosity.

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  • Measuring human perceptions of a large-scale urban region using machine learning

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  • Hdbscan

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  • Measuring residents’ perceptions of city streets to inform better street planning through deep learning and space syntax

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  • International variation in neighborhood walkability, transit, and recreation environments using geographic information systems

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  • Measuring the Unmeasurable

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  • Important places in landscape – investigating the determinants of perceived landscape value in the suburban area of Wrocław, Poland

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  • How does spatial structure affect psychological restoration? A method based on graph neural networks and street view imagery

    Open Access•Haoran Ma, Yan Zhang et al.•Landscape and Urban Planning•2024

  • Greener view, safer drive

    Open Access•Wenyan Xu, Jwu‐Sheng Hu et al.•Landscape and Urban Planning•2024

  • Measuring heterogeneous perception of urban space with massive data and machine learning

    Open Access•Tomás Ramírez, Tomas Eduardo Ramirez et al.•Landscape and Urban Planning•2021

  • Evaluating the subjective perceptions of streetscapes using street-view images

    Open Access•Yoshiki Ogawa, Takuya Oki et al.•Landscape and Urban Planning•2024

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

    Open Access•Yuhao Kang, Jonatan Abraham et al.•Landscape and Urban Planning•2023

  • Street view imagery in urban analytics and GIS

    Open Access•Filip Biljecki, Koichi Ito•Landscape and Urban Planning•2021

  • Urban greenness, mixed land-use, and life satisfaction

    Open Access•Wenjie Wu, Wendy Y Chen et al.•Landscape and Urban Planning•2022

  • A novel walkability index using google street view and deep learning

    Open Access•Donghwan Ki, Zhenhua Chen et al.•Sustainable Cities and Society•2023

  • Multiscale analysis of human social sensing of urban appearance and its effects on house price appreciation in Wuhan, China

    Open Access•Jia Jia, Xiaoqing Zhang et al.•Sustainable Cities and Society•2022

  • Visual Aesthetic Quality Assessment of Urban Forests

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  • The Timing and Aesthetics of Public Engagement

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  • Measuring human perceptions of streetscapes to better inform urban renewal

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  • Measuring the age-friendliness of streets' walking environment using multi-source big data

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  • Quantitative evaluation of attraction intensity of highway landscape visual elements based on dynamic perception

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Citations per year5
Citation span2026 - 2026 (1)
Highly citedNo

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