Automated Runway Pavement Inspection & Maintenance Planning
End-to-end runway condition assessment — from high-resolution drone survey to automated defect detection, PCI computation, maintenance optimization, and budget planning.
The Challenge
Airport pavement condition directly impacts flight safety and operational continuity. Runways and taxiways degrade over time — cracking, rutting, weathering, joint failures, and surface distress accumulate and interact in complex ways. Traditional inspection methods involve manual surveys that are time-consuming, subjective, and produce results that are difficult to translate into actionable maintenance plans. Computing a reliable Pavement Condition Index (PCI) and linking it to optimized maintenance schedules and realistic budgets requires a level of consistency and throughput that manual processes cannot deliver at scale.
Our Approach
We built a system that takes high-resolution drone orthophotography (DOF) as input and delivers a complete pavement management workflow. Deep learning models automatically detect and classify surface distresses across the full survey area. Detected defects feed into standardized PCI computation, producing condition scores per section. A maintenance optimization module then generates repair strategies, scheduling recommendations, and multi-year budget projections — all accessible through a web platform alongside automated inspection reports and georeferenced QGIS layers.
Key Results
Technologies Used
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