Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety
This work addresses urban road safety planning by providing a scalable tool for targeted interventions, though it is incremental as it combines existing methods like XGBoost and causal inference in a new application.
The authors tackled the problem of quantifying causal impacts of streetscape features on urban road safety by proposing Semantic4Safety, a framework that uses zero-shot semantic segmentation on street-view imagery to derive indicators and analyzes about 30,000 accident records in Austin, finding that features like drivable area and emergency space reduce risk while visual openness increases it.
Street-view imagery (SVI) offers a fine-grained lens on traffic risk, yet two fundamental challenges persist: (1) how to construct street-level indicators that capture accident-related features, and (2) how to quantify their causal impacts across different accident types. To address these challenges, we propose Semantic4Safety, a framework that applies zero-shot semantic segmentation to SVIs to derive 11 interpretable streetscape indicators, and integrates road type as contextual information to analyze approximately 30,000 accident records in Austin. Specifically, we train an eXtreme Gradient Boosting (XGBoost) multi-class classifier and use Shapley Additive Explanations (SHAP) to interpret both global and local feature contributions, and then apply Generalized Propensity Score (GPS) weighting and Average Treatment Effect (ATE) estimation to control confounding and quantify causal effects. Results uncover heterogeneous, accident-type-specific causal patterns: features capturing scene complexity, exposure, and roadway geometry dominate predictive power; larger drivable area and emergency space reduce risk, whereas excessive visual openness can increase it. By bridging predictive modeling with causal inference, Semantic4Safety supports targeted interventions and high-risk corridor diagnosis, offering a scalable, data-informed tool for urban road safety planning.