Remote Sensing of Transportation Assets Using Drones and Artificial Intelligence

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.


  • English


  • Status: Active
  • Funding: $366000
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108
  • Project Managers:

    Tolliver, Denver

  • Performing Organizations:

    Upper Great Plains Transportation Institute

    North Dakota State University
    1320 Albrecht Boulevard
    Fargo, ND  United States  581052
  • Principal Investigators:

    Bridgelall, Raj

    Tolliver, Denver

  • Start Date: 20210917
  • Expected Completion Date: 20240731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Source Data: MPC-665

Subject/Index Terms

Filing Info

  • Accession Number: 01782607
  • Record Type: Research project
  • Source Agency: Mountain-Plains Consortium
  • Contract Numbers: 69A3551747108
  • Files: UTC, RIP
  • Created Date: Sep 22 2021 2:04PM