MPC
Project Details

Title:	Remote Sensing of Transportation Assets Using Drones and Artificial Intelligence
Principal Investigators:	Raj Bridgelall and Denver Tolliver
University:	North Dakota State University
Status:	Active
Year:	2021
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-665
RiP #:	01782607
RH Display ID:	159533
Keywords:	artificial intelligence, asset management, drones, remote sensing

Abstract

The rapid acquisition, processing, and visualization of data can enhance the effectiveness of transportation planning, traffic operations, and incident response. Hence, agencies can benefit from data sensed remotely from transportation assets like roads, bridges, railroads, pipelines, freight yards, rights-of-way, and other essential assets such as signs and signals. So far, however, the remote sensing of transportation assets has been based primarily on satellite images, video, or photography from manned aircrafts. The commercial development of unmanned aircraft systems, commonly called drones, can enable remote sensing with many advantages because drones can generate more information, faster, at lower cost, and more safely. The intersection of artificial intelligence (AI) methods and sensor packages can further enhance those advantages. Therefore, the goal of this research is to distill and identify essential characteristics at the intersection of drones, sensors, and AI methods to advance applications in the remote sensing of transportation assets.

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