Proposal of an Integrated Method of Unmanned Aerial Vehicle and Artificial Intelligence for Crack Detection, Classification, and PCI Calculation of Airport Pavements

Valerio Perri, Misagh Ketabdari, Stefano Cimichella, Maurizio Crispino and Emanuele Toraldo ()
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Valerio Perri: Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Misagh Ketabdari: Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Stefano Cimichella: Logistics Command—Infrastructure Service, Air Force, Viale Dell’Università 4, 00185 Rome, Italy
Maurizio Crispino: Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Emanuele Toraldo: Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy

Sustainability, 2025, vol. 17, issue 7, 1-19

Abstract: Assessing the condition of airport pavements is essential to ensure operational safety and efficiency. This study presents an innovative, fully automated approach to calculate the Pavement Condition Index (PCI) by combining UAV-based aerial photogrammetry with advanced Artificial Intelligence (AI) techniques. The method follows three key steps: first, analyzing orthophotos of individual pavement sections using a custom-trained AI model designed for precise crack detection and classification; second, utilizing skeletonization and semantic mask analysis to measure crack characteristics; and third, automating the PCI calculation for faster and more consistent evaluations. By leveraging high-resolution Unmanned Aerial Vehicle (UAV) imagery and advanced segmentation models, this approach achieves superior accuracy in detecting transverse and longitudinal cracks. The automated PCI calculation minimizes the need for human intervention, reduces errors, and supports more efficient, data-driven decision-making for airport pavement management. This study demonstrates the transformative potential of integrating UAV and AI technologies to facilitate infrastructure maintenance and enhance safety protocols.

Keywords: pavement condition index (PCI); UAV-based aerial photogrammetry; airport pavement management; automated crack detection; AI-driven infrastructure analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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