Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation Through Multiscale Satellite Imagery and Self-Supervised Cross-Matching

Abstract

Traffic accidents threaten human lives and impose substantial financial burdens annually. Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and saving lives. This article proposes an innovative approach that utilizes multiscale satellite imagery and self-supervised learning for fatal crash risk estimation. By integrating multiscale imagery, our network captures diverse features at different scales, encompassing observations of surrounding environmental factors in low-resolution images that cover larger areas and learning detailed ground-level information from high-resolution images. One advantage of our work is its sole reliance on satellite imagery data, making it an efficient and practical solution, especially when other data modalities are unavailable. With the ability to accurately estimate fatal crash risk, our method exhibits a potential for enhancing road safety, optimizing infrastructure planning, preventing accidents, and ultimately saving lives.

Keywords

• Accident prediction
• aerial image
• remote sensing
• road safety
• self-supervised learning (SSL)
• smart cities