Digitalization and Automation of Runway Inspection Using Unmanned Aerial Vehicles

TL;DR

This paper introduces a system using drones and AI to automatically inspect and assess runway conditions, offering a digital alternative to manual checks.

Contribution

The novel contribution is an end-to-end framework combining UAV imagery, deep learning, and GIS for automated runway inspection.

Findings

The system successfully detects and maps multiple defect types across the full runway.

A georeferenced condition map was produced, supporting maintenance prioritization.

Validation at Zadar Airport demonstrated the system's scalability and practicality.

Abstract

This paper presents an end-to-end framework for automated inspection and condition assessment of airport runway pavement using UAV-acquired imagery. The proposed approach integrates Unmanned Aerial Vehicle (UAV)-based data collection, deep learning-based pixel-level semantic segmentation of surface defects, and Geographic Information System (GIS)-based spatial aggregation to generate a georeferenced digital representation of airfield pavement condition. Multiple safety-critical defect types are detected and localized at pixel resolution, while spatially referenced processing enables a Pavement Condition Index (PCI)-inspired condition assessment based on defect density within predefined sampling units. The framework is validated through a real-world case study at Zadar Airport, where the entire runway was surveyed using high-resolution UAV imagery. The results demonstrate the system’s capability to identify and map multiple defect categories across the full runway extent and to produce a coherent, runway-scale condition map supporting maintenance prioritization and decision-making. Overall, the proposed solution provides a scalable, data-driven alternative to traditional manual runway inspection workflows and establishes a practical foundation for digital condition monitoring of airport pavement infrastructure.

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