Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety

• URL: http://arxiv.org/abs/2510.15434v1
• Date: Fri, 17 Oct 2025 08:45:28 GMT
• Title: Semantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety
• Authors: Huan Chen, Ting Han, Siyu Chen, Zhihao Guo, Yiping Chen, Meiliu Wu,
• Abstract summary: We propose a framework that applies zero-shot semantic segmentation to street-view imagery to derive 11 interpretable streetscape indicators.<n>We analyze approximately 30,000 accident records in Austin to uncover accident-type-specific causal patterns.<n>By bridging predictive modeling with causal inference, Semantic4Safety supports targeted interventions and high-risk corridor diagnosis.
• Score: 14.300819977717708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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