Identification of Pathologies in Pavements by Unmanned Aerial Vehicle (UAV): A Systematic Literature Review

The identification and monitoring of pavement pathologies are critical for maintaining road infrastructure and ensuring transportation safety. As traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, in recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool for pavement condition assessment due to their mobility, efficiency, and ability to capture high-resolution imagery and multi-sensor data. This Systematic Literature Review aims to synthesize and evaluate existing research on the use of UAV for identifying pavement pathologies, such as cracks, potholes, rutting, and surface degradation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, publications were screened and selected across major academic databases such as Scopus and Web of Science. A total of 361 relevant articles published from 2020 to July 2025 were identified and analyzed using bibliometric overview. And a full-text synthesis and qualitative analysis was performed on a subset of 108 studies, which met the quality assessment criteria. The review categorizes the UAV systems, computer vision approaches, pathology types, and pavement materials examined in the studies. The findings indicate a growing trend in the use of UAV and computer vision techniques for pavement pathology detection, along with evolving preferences for UAV platforms, analytical approaches, and targeted pathology categories over time. This review highlights current gaps and outlines future research directions to advance UAV-based pavement pathology identification as a viable and reliable alternative to conventional inspection methods.