Surface Condition Assessment of Unpaved Roads Through the Use of Unmanned Aerial Vehicle

Identification and extraction of unpaved road surface parameters from images are emerging and essential to effective road surface condition survey. The vulnerability of unpaved roads—lacking protective surface, makes the situation more heighten. This paper seeks to illustrate an approach to identify and extract road surface condition parameters from remotely sensed imageries obtained from unpaved roads. To accomplish this, an Unmanned Aerial Vehicle (UAV) was used to capture images on an unpaved road at Ofirikrom Municipality in Ashanti Region of Ghana, where they were pre-processed, and analysed by building digital elevation model (DEM) and orthomosaic. The processed image was validated with a conventional field taken in situ. Both imagery and conventional field method were able to extract the same defects. After analysis, three defects varied from the rating while 6 were similar for both rating. Defect count which relates to sectional analysis of defects also exhibits similar predictions for both approaches. These findings illustrate the potential use of remotely sensed imagery from UAV to identify, extract, and measure road surface condition parameters for unpaved roads. The results can facilitate the monitoring operation of road agencies in the country towards low-cost and effective maintenance of roads.