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Digitalization and Automation of Runway Inspection Using Unmanned Aerial Vehicles.

Marios Krestenitis1, Alexandros Petropoulos1, Ilias Koulalis1

  • 1Centre for Research and Technology-Hellas (CERTH), 57001 Thessaloniki, Greece.

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Summary
This summary is machine-generated.

Automated airport runway inspection uses Unmanned Aerial Vehicle (UAV) imagery and deep learning to detect pavement defects. This digital approach offers a scalable alternative to manual methods for condition assessment and maintenance planning.

Keywords:
AI-based defect detectionUAVsairport runway inspectiondigital twinspredictive maintenance

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Area of Science:

  • Civil Engineering
  • Geospatial Science
  • Computer Science

Background:

  • Traditional airport runway inspections are manual, time-consuming, and subjective.
  • Accurate pavement condition assessment is critical for aviation safety and operational efficiency.
  • Existing methods struggle with scalability and detailed spatial defect mapping.

Purpose of the Study:

  • To develop an automated, end-to-end framework for airport runway pavement inspection and condition assessment.
  • To integrate Unmanned Aerial Vehicle (UAV) data collection with deep learning and GIS for pavement analysis.
  • To create a georeferenced digital pavement condition map for maintenance prioritization.

Main Methods:

  • Utilized Unmanned Aerial Vehicle (UAV) for high-resolution aerial data acquisition.
  • Applied deep learning-based pixel-level semantic segmentation to identify and classify pavement surface defects.
  • Employed Geographic Information System (GIS) for spatial aggregation and Pavement Condition Index (PCI)-inspired assessment.

Main Results:

  • Successfully detected and localized multiple safety-critical runway pavement defect types at pixel resolution.
  • Generated a georeferenced digital representation of airfield pavement condition.
  • Produced a runway-scale condition map demonstrating the system's capability for maintenance decision-making.

Conclusions:

  • The proposed framework offers a scalable, data-driven alternative to traditional manual runway inspection.
  • The system provides a practical foundation for digital condition monitoring of airport pavement infrastructure.
  • Automated inspection enhances efficiency and accuracy in airfield pavement management.