Implications of Artificial Intelligence for Assessing the Built Environment
Bibliographic Data
| ID | 15318902 |
|---|---|
| Authors | Fereshteh Moradi (0000-0002-6584-3282, University of Technology Sydney, corresponding author), Nimish Biloria (0000-0003-3152-5187, University of Technology Sydney) |
| Year | 2025 |
| Volume | 32 |
| Issue | 3 |
| Pages | 163-191 |
| Publication date | 2025-05-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Urban Technology (JOURNAL) |
| Journal identifiers | ISSN: 1063-0732 • E-ISSN: 1466-1853 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10630732.2025.2468142 |
| OpenAlex | W4410611451 |
| Language | EN |
| Citations received | 3 |
| References cited | 109 |
BIM and Construction Integration · Computer Science · Engineering · Infrastructure Maintenance and Monitoring · Noise Effects and Management · Artificial Intelligence
Assessing bikeability with street view imagery and computer vision
Faster R-CNN
Systematic social observation of children’s neighborhoods using Google Street View
Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities
Measuring visual enclosure for street walkability
Google Street View
ImageNet classification with deep convolutional neural networks
Focal Loss for Dense Object Detection
Assessing street-level urban greenery using Google Street View and a modified green view index
Using Google Street View to Audit Neighborhood Environments
Deep Learning the City
You Only Look Once
The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions
A machine learning-based method for the large-scale evaluation of the qualities of the urban environment
Deep learning
Method for Applying Crowdsourced Street-Level Imagery Data to Evaluate Street-Level Greenness
Panoramic Street-Level Imagery in Data-Driven Urban Research
Urban Planning and the Smart City
Combining visual and noise characteristics of a neighborhood environment to model residential satisfaction
“Perception bias”
Predicting perceptions of the built environment using GIS, satellite and street view image approaches
Street view imagery in urban analytics and GIS
Research trend prediction in computer science publications
An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains
How are Neighborhood and Street-Level Walkability Factors Associated with Walking Behaviors? A Big Data Approach Using Street View Images
Marked crosswalks in US transit-oriented station areas, 2007–2020
Urban-i
The visual quality of streets
Leveraging Street Level Imagery for Urban Planning
Analyze the usage of urban greenways through social media images and computer vision
Urban AI
Characterizing the perception of urban spaces from visual analytics of street-level imagery
Smart urbanism and smart citizenship
Machine Learning Algorithms for Urban Land Use Planning
The Emergence of Artificial Intelligence in Anticipatory Urban Governance
Systematic review of the use of Google Street View in health research
Examining the role of urban street design in enhancing community engagement
Neighbourhood walkability
Children's spaces in coastal cities
Using Google Earth to conduct a neighborhood audit
Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (Canvas) to measure health-related neighborhood conditions
A Local View of Informal Urban Environments
The rise of AI urbanism in post-smart cities
Neighbourhood land use features, collective efficacy and local civic actions
Selling Smartness
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |