Road Pavement Distress Extraction Using UAV Imageries (Case Study: Alvar-e-Sofla Village near Tabriz)

Aging and rupture phenomena are important factors which decrease the useful life of asphalt. During the recent decade, by using the images with high spatial accuracy (less than 10 cm) the qualitative monitoring of roads’ surfaces has been achieved. The present study has been performed to evaluate the aging and rupture phenomena on asphalt surface and to evaluate the DSM, extracted from drone images, in asphalt quality control in Alvar-e- Sofla village, Tabriz, Iran. Hyperspectral imaging for three kinds of asphalt (less than 2 years, 4 to 7 years and more than 10 years old) revealed that the 2-year old asphalt has always lower graph than the others because, after a while, the asphalt bitumen losses its quality and gets lighter and thus its surface reflection increases. Calibration of selected learning points for distress shows overall accuracy of 95% and CAPA coefficient of 95, which reveals the high accuracy and correctness of the results. Also, high spatial accuracy (7.5 cm) shows the ability to study type 2 and 3 ruptures, in addition to cracking-depth evaluation and stress via these images at centimeter scale. Results showed that the obtained DSM from overlapping of drone images has the ability to extract lateral slopes of pedestrian and streets, which are applicable in evaluation of asphalt subduction amount during the time. If this problem is fixed and the road slope is standardized, practically the flooding events in cold seasons will be reduced and useful life of asphalt will be increased.