Papers › Urban Perception: Can we understand why a street is safe?
Urban Perception: Can we understand why a street is safe?
Felipe Moreno-Vera, Braham Lavi, Jorge Poco
The importance of urban perception computing is relatively growing in machine learning, particularly in related areas to Urban Planning and Urban Computing. This field of study focuses on developing systems to analyze and map discriminant characteristics that might directly impact the city’s perception. In other words, it seeks to identify and extract discriminant components to define the behavior of a city’s perception. This work will perform a street-level analysis to understand safety perception based on the “visual components”. As our result, we present our experimental evaluation regarding the influence and impact of those visual components on the safety criteria and further discuss how to properly choose confidence on safe or unsafe measures concerning the perceptional scores on the city street levels analysis.
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 | Place Pulse 2.0 | ResNet151 | AUC | 76.8 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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