Papers › Predicting city safety perception based on visual image content
Predicting city safety perception based on visual image content
Sergio Acosta, Jorge E. Camargo
Safety perception measurement has been a subject of interest in many cities of the world. This is due to its social relevance, and to its effect on some local economic activities. Even though people safety perception is a subjective topic, sometimes it is possible to find out common patterns given a restricted geographical and sociocultural context. This paper presents an approach that makes use of image processing and machine learning techniques to detect with high accuracy urban environment patterns that could affect citizen's safety perception.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Safety Perception Recognition | Google Street Images | CNN | Accuracy | 81% | #1 of 1 | Archive leaderboard | report |
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