Application of machine learning algorithms on satellite imagery for road quality monitoring: an alternative approach to road quality surveys

This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region. The paper notes that collecting information on road quality is difficult, particularly in harder to reach middle-and low-income areas, and explains why this method offers an alternative. It shows how the study’s preliminary algorithm was created using satellite imagery and existing road roughness data from the Philippines. It assesses the accuracy rate and finds it sufficient for the preliminary identification of poor to bad roads. It notes that additional enhancements are needed to increase its prediction accuracy and make it more robust.