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.
Language
- English
Project
- Status: Completed
- Funding: $366000
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Contract Numbers:
69A3551747108
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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:
North Dakota State University
Fargo, ND United States 58108 -
Project Managers:
Tolliver, Denver
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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: 20240822
- USDOT Program: University Transportation Centers Program
- Source Data: MPC-665
Subject/Index Terms
- TRT Terms: Artificial intelligence; Asset management; Drones; Remote sensing
- Subject Areas: Aviation; Bridges and other structures; Data and Information Technology; Maintenance and Preservation; Transportation (General);
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