Assessment of vertical clearance on highways is an integral step to ensuring that design standards are met throughout the service life of the highway. The assessment enables timely intervention, in case clearance requirements are not met, thereby extending the service life of structures and avoiding prohibitive maintenance costs due to damage which could occur to those overhead objects in case of collisions. That being said, before clearance can be assessed at overhead objects, these objects must first be detected, inventoried and classified. Unfortunately, manual procedures to collect such information on highways are unsafe, time consuming, labour intensive and, in some cases, impractical. This is particularly true when information is required on a network-level. This paper proposes a novel technique by which overhead objects could be automatically detected, classified and inventoried using mobile LiDAR data. Moreover, the proposed algorithm also provides an estimate of the clearance at those objects. The technique involves defining the road trajectory of the highway and then using search algorithms to detect overhead structures. Further, the algorithm employs clustering tools to classify the detected structures into different objects (eg: bridges vs power lines). The algorithm also yields an estimate of the clearance all detected overhead objects. The algorithm is tested on two different highway segments at the province of Alberta and was successful in detecting all overhead structures on those highways, and in providing a decent estimate of the clearance at all those structures.