The monitoring of pavement distress takes a key role with respect to the functionality of a road structure, which is necessary to schedule maintenance works, optimizing the available resources. Due to the high payload capacity and to the weight loss, nowadays, the UAV (Unmanned Aerial Vehicle) systems are having a great success in the infrastructure field. To evaluate the surface condition, it is possible to use 3D models obtained through UAV data. Different solutions exist on the market and research program that are pushing for faster and more economical solutions using the innovative technology of surveying and data computation. The research aims to compare the rough measurements of the surface of the road, obtain through the standard methods and UAV data. We conducted a test on a portion of an urban local road network, the straight stretch of road is approximately 200 meters long, inside the University Campus of Salerno. The test area has different types of pavement distress, good enough for a validation test. The potential of small size and weight UAV system (DJI Mavic 2 Pro) was analyzed. In photogrammetric flight plane, different flight heights were set. The UAV was set to a target altitude of 10, 15, 20 and 25 meters, above the point of take-off (road surface) in order to obtain different resolutions (2,3,5 and 6 mm respectively) and analyze the differences. For the comparison, a reference roughness measurement was carried out using the Static Level Method, according to ASTM E1364. The estimated Roughness was conducted using the International Roughness Index (IRI). The analyses were carried out on four different part of tire tracks, taking into account the vehicles generally travelling on that route. The results showed a good correlation (r > 0.75), particularly that the best results were obtained from the photogrammetric flights at 10- and 15-meters flight heights. Even more so, it is possible to notice that the differences are not very significant; the results dictated by IRI place the different surfaces in the same classes of judgment.
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