Pavement condition index (arezzo)

Pavement Condition Index (PCI) Survey & Annotation Specialist – Freelance AI Trainer Project Are you a pavement professional or roadway inspector with hands-on experience conducting or interpreting Pavement Condition Index (PCI) surveys? Advanced AI systems are being developed to understand roadway conditions, interpret inspection imagery, and support infrastructure maintenance planning. High-quality training data based on real PCI survey practices is essential to ensure these systems accurately recognize pavement distress and condition ratings. This project relies on your expertise to help train and evaluate next-generation AI models.
In this project, you will focus specifically on PCI-related workflows and annotation tasks. You will review roadway images, inspection records, and AI-generated outputs to determine whether pavement distress types, severity levels, and condition assessments are accurate and consistent with PCI methodology. A central part of the work involves annotating or validating pavement features and distress patterns so models can better learn to identify cracking, rutting, spalling, patching, weathering, and other common pavement conditions.
On a typical day, you may analyze roadway imagery or survey excerpts, validate AI-generated classifications of pavement distress, and provide written feedback explaining where models misidentify features or misinterpret severity levels. You may also help clarify how inspectors distinguish between similar distress categories, how sampling units are evaluated, and how real-world field conditions influence PCI scoring and reporting.
This project is well suited for pavement engineers, civil or transportation engineers, DOT specialists, highway inspectors, and other professionals who have conducted or reviewed PCI surveys or pavement condition assessments. Experience working with inspection imagery, condition reports, or roadway maintenance planning is highly valuable. Clear written communication and the ability