The assessment of bridge deck condition demands the development of rapid and efficient diagnosis, prognosis, and repair techniques to safely and cost-effectively extend the life-cycle of our transportation infrastructure. Without the ability to identify and characterize deficiencies at their early stages, prognosis and various repair strategies simply cannot be brought to bear effectively. Therefore, there is a pressing need for the implementation of wireless, non-contact, or remote sensors that can provide rapid and cost-effective data. LiDAR sensors have the ability to capture dense point clouds that define the geometry of objects in a remote (non-contract) manner. This project evaluates the impact of capturing point cloud data of bridge deck top surfaces to enable a rapid screening method by identifying characteristics of early stage deterioration.