Image-based large language model approach to road pavement monitoring

Abstract

Accurate and rapid assessment of pavement surface condition is essential for maintaining transportation safety and minimizing vehicle wear. Manual pavement inspections are subjective and time-consuming, and machine learning methods typically require large labeled datasets. This study introduces an innovative zero-shot learning method that leverages large language models’ (LLMs) image analysis and natural-language understanding capabilities for accurate road condition assessment. Prompts were designed in alignment with the pavement surface condition index criteria to generate multiple evaluation models, which were then compared against official scores to identify an optimized configuration. Tests conducted using Google Street View imagery indicate that the optimized LLM-based model achieves a mean absolute error of 1.07 on a 0–10 scale, outperforming expert evaluations. The proposed approach enables rapid, accurate, and consistent assessments without the need for labeled data, demonstrating the transformative role of LLMs in automating infrastructure monitoring and emphasizing the importance of structured prompt engineering for reliable performance.