###### Abstract
This latter feature is extremely important in cases of newly constructed road pavement structures, as the drilling of freshly built layers is not allowed, even for construction quality control purposes. A detailed review of non-destructive tests can be found in the works of [PERSON] and [PERSON] (2008) [13] and [PERSON] et al. (2022) [14]. To implement non-destructive tests, the utilization of unmanned aerial vehicles (UAVs) represents a very attractive technology due to their simple use, low maintenance costs, high operational flexibility, and ability to be used at almost any location. Drones, as a common type of UAVs, are powerful tools in several scientific fields. Due to their versatility, multiple types of sensors can be equipped, such as passive sensors (optical cameras) and/or active sensors (light detection and ranging (LIDAR) radar) [15; 16; 17]. Cameras are the most common payloads in drones as they are the most accessible, are less costly, and offer multifield sensors. Traditionally, the processes of detecting any type of damage to a pavement are implemented manually or utilized in a road vehicle at a high labour cost [18]. Road pavement distresses, such as potholes and cracks, may appear on the road surface [16]. A significant number of studies have focused on the utilization of drone images to monitor and analyse pavement surface conditions (such as [19]) and automate crack detection from pavement surface imagery [20]. Additionally, machine learning algorithms or multi-agent architecture [18] are used as a primary means by which to evaluate such photos and automatically identify cracks and/or potholes [15; 17]. Multispectral imagery, which presents spatial and spectral features of objects, has been widely used in remote sensing applications. [PERSON] et al. (2018) [16] evaluated UAV-based multispectral pavement images to differentiate normal pavement from damaged pavement (e.g., cracks and potholes) by applying machine learning algorithms. Hyperspectral sensors are camera-like sensors that capture imagery outside of the visible spectrum. As asphalt pavements deteriorate, changes in asphalt composition arise, and, ultimately, the reflectivity of that alteration allows for the identification of a pavement's defects in its asphalt [15; 17]. From the presented devices and methods in the above-mentioned works, this paper deals only with ground-penetrating radar (GPR) measurement technology. In a comparison of traditional detection methods of highway pavement conditions and GPR, [PERSON] et al. (2008) [21] concluded that GPR can provide continuous and non-destructive measurements without disturbing the pavement structure. Among various non-destructive testing techniques (NDTs), GPR is considered one of the most efficient instruments due to its high usage flexibility and result reliability [22]. Furthermore, it is easy to use, is not disruptive to traffic, and can be used as a multi-application device in road transport infrastructure and civil engineering applications. The main applications of GPR for pavement assessment are measurement of pavement layers' thickness, subsidence, and sinkholes; rebar detection/localization; and detection of moisture damage and crack air voids [23]. A major advantage of GPR is its capability of collecting data on a centimetre scale at driving speeds when using air-coupled antennas. A GPR assessment can establish accurate, continuous profiles of pavement layers and has the capability to detect major causes of structural failure at traffic speed [24]. This method is suitable not only for the assessment of the condition of deteriorated road sections but also for quality control of newly constructed sections. Nevertheless, it is mainly used for the former purpose. The reason for such application is its capability of acquiring large amount of continuous spatial data without disturbing traffic [25].