Collecting Decision Support System Data through Remote Sensing of Unpaved Roads

Abstract

Unpaved roads make up roughly 33% of the road system within the United States and are vitally important to rural communities for transport of people and goods. Effective asset management of unpaved roads requires frequent inspections to determine the roads’ condition and the appropriate preventive maintenance or rehabilitation. The major challenge with managing unpaved roads is low-cost collection of condition data that are compatible with a decision support system (DSS). The advent of cheap, reliable remote-sensing platforms such as unmanned aerial vehicles along with the development of commercial off-the-shelf image analysis algorithms provides a revolutionary opportunity to overcome these data volume and efficiency issues. By taking advantage of these technological leaps, a market-ready system to detect unpaved road distress data compatible with a DSS was developed. The system uses aerial imagery that can be collected from a remote-controlled helicopter or manned fixed-wing aircraft to create a three-dimensional model of sensed road segments. Condition information on potholes, ruts, washboarding, loss of crown, and float aggregate berms is then detected and characterized to determine the extent and severity of the distress. Once detection and analysis are complete, the data are imported into a DSS based on a geographic information system (Road-soft) for use by road managers to prioritize preventive maintenance and rehabilitation efforts.