Rockfall events can pose a direct and significant threat to road safety, particularly in mountainous and coastal regions where slope instability can cause sudden and hazardous disruptions. Conventional inspection methods are less popular nowadays because of accessibility constraints, safety risks for human personnel, and insufficient monitoring frequency. Additionally, rockfall events can significantly impact pavement performance by accelerating surface deterioration and structural damage. Thus, early identification of high-risk zones enables timely interventions, helping to preserve pavement integrity and prevent the development of extensive failures that threaten human life, disrupt mobility and require costly rehabilitation.

This study explores the use of Unmanned Aerial Vehicle (UAV) sensing as a Non-Destructive Testing (NDT) approach for early detection and assessment of rockfall-prone areas near roadways, aiming to enhance safety and ensure uninterrupted road traffic. In particular, an UAV is employed to acquire high-resolution multimodal data, including optical imagery for Structure-from-Motion (SfM), LiDAR, and thermal data, over a vulnerable road section in North Greece. The proposed framework focuses on qualitative and semi-quantitative interpretation of aerial data to identify indicators of instability, such as fractured rock masses, debris accumulation, and evolving slope conditions.

In terms of its contribution, the presented approach supports rapid deployment and repeatable inspections, consistent with non-invasive monitoring principles. UAV-assisted inspections provide a flexible and scalable solution that contributes to life safety and road infrastructure resilience, by enabling timely interventions and supporting the targeted placement of protective measures, such as rockfall barriers and wire meshes, along high-risk sections.