This research introduces a novel approach that leverages large language models to assess road conditions from images without requiring labeled training data.

• Eliminates the need for extensive labeled datasets that traditionally limit pavement monitoring systems
• Achieves comparable accuracy to conventional methods through zero-shot learning capabilities
• Demonstrates adaptability across diverse road conditions and environments
• Provides objective, consistent, and scalable pavement condition assessments

For engineering teams, this breakthrough means faster deployment of monitoring systems, reduced costs, and more efficient maintenance planning—ultimately improving transportation safety and infrastructure management.

Zero-Shot Image-Based Large Language Model Approach to Road Pavement Monitoring