Road surface condition evaluation is a critical component for the operation and maintenance of road infrastructure. The situation is more heightened for unpaved roads as they lack protective hard surfaces such as concrete or asphalt. New methods are now emerging for the identification and extraction of road surface condition parameters from remotely sensed imagery. Accordingly, this study illustrates an approach to identify and extract road surface condition parameters from remotely sensed imageries obtained from unpaved roads with unique environmental characteristics. An Unmanned Aerial Vehicle (UAV) was used to capture images on unpaved roads where they were pre-processed, and analysed by building a digital elevation model (DEM) and orthomosaic. The processed imagery was validated with a conventional field measurement of road defects. The number of road defects identified and their severity was similar for both approaches. The defects counts were similar for all except one defect which vary for both approaches. The predictions of the extent of defect severity identified as percent count were exact – vegetation encroachment had the highest 100% and depression had the lowest of 25%. 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.