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Related Experiment Video

Updated: Aug 8, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

An interpretable machine learning framework for measuring urban perceptions from panoramic street view images.

Yunzhe Liu^1,2, Meixu Chen³, Meihui Wang⁴

¹Informal Cities, Oxford Martin School, University of Oxford, Oxford OX1 3BD, UK.

|February 27, 2023

Summary

This study introduces an interpretable machine learning framework to analyze urban perceptions from street view images. The method extracts neighborhood-level insights into wealth, safety, and beauty, aiding urban planning.

Keywords:

Artificial Intelligence Environmental sciences

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Last Updated: Aug 8, 2025

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Area of Science:

Urban Analytics
Computer Vision
Machine Learning

Background:

Street View Images (SVIs) and deep learning enable urban perception analysis.
Existing 'black-box' frameworks lack interpretability for urban planning support.

Purpose of the Study:

Propose an interpretable 5-step machine learning framework for urban perception extraction from SVIs.
Enhance feature and result interpretability in urban streetscape analysis.

Main Methods:

Utilized the MIT Place Pulse dataset for training and validation.
Developed a systematic framework to extract six dimensions of urban perception.
Applied the framework to panoramic SVIs for neighborhood-level analysis.

Main Results:

Successfully extracted six urban perception dimensions: wealth, boredom, depression, beauty, safety, and liveliness.
Demonstrated practical utility by visualizing urban perceptions in Inner London at the Output Area (OA) level.
Validated framework outputs against real-world crime rate data.

Conclusions:

The proposed framework offers an interpretable approach to analyzing urban perceptions from SVIs.
This method enhances the value of urban analytics for planning support tools.
The systematic extraction and visualization of perceptions provide actionable insights for urban development.