Structural Health Monitoring (SHM)-based inspections of buildings, roadways, and structures have been conducted manually by qualified individuals. This process often involves the manual vision-based inspection of various elements of the structure to ascertain the level of deterioration associated with the structure. This analysis supports recommendations by engineers as to whether the structure needs to be rehabilitated, redesigned, or replaced as well as an estimate of the appropriate timeline to do so. However, traditional SHM have several limitations due to their manual, human-based nature including: high economic costs, logistical constraints (due to limited access to the structure), increased time durations of inspections, and analysis bias due to human involvement in the process. As such, there has been a paradigm shift to involve advanced robotic technology, including unmanned aerial vehicles (UAV), unmanned ground vehicles, cameras, augmented reality, and LiDAR, to help mitigate the costs of conducting SHM inspections while improving their efficiencies. This paper demonstrates the positive benefits of UAV-based inspections for road condition assessment for structural health monitoring. A DJI Matrice 300 Real Time Kinematics (RTK) UAV with a P1 Zenmuse Camera is implemented to conduct a mapping exercise for a 3.5 km stretch of asphalt road located outside of London, Ontario, Canada. Best practices for conducting UAV flights for RGB 2D mapping exercises for SHM applications are provided. Furthermore, a description of the drone setup and the parameters chosen for forming the flight mission path planning and the data collection process is summarized. From the inspection, both a 2D map and a 3D model were developed for the road and right-of-way using commercially available software. Lastly, use cases for the data and models collected and developed through this study are discussed, emphasizing existing SHM inspection frameworks for industry-based practices.