Rotorcraft Landing Sites Identification – Scaling and Generalization of the AI Model
The primary goal of this proposal is to address the challenging problem of automatic identification of helipads and landing sites using the machine and deep learning algorithms. This project’s deliverable is an artificial intelligence (AI)-based system for the identification of helipads, heliports, and landing site infrastructure from satellite images. The intended outcome of the AI model is to automate the process of identification of landing sites for rotorcrafts from the Google Earth satellite imagery. This system is expected to achieve landing site identification accuracy equal to or higher than that of a trained human operator at a fraction of time and resources. Once developed, the AI system would allow the Federal Aviation Administration (FAA) to regularly update its databases without delays and, as a result, the databases of FAA could be used by any mission, including “Helicopter Air Ambulance missions to rural communities.”
Language
- English
Project
- Status: Active
- Funding: $120846
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Contract Numbers:
CAIT-UTC-REG54
69A3551847102
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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:
William J. Hughes Technical Center
Atlantic City International Airport
Atlantic City, nj United States 08405Center for Advanced Infrastructure and Transportation
Rutgers University
100 Brett Road
Piscataway, NJ United States 08854-8058 -
Project Managers:
Johnson, Charles (Cliff)
Szary, Patrick
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Performing Organizations:
Rowan University
College of Engineering
201 Mullica Hill Road
Glassboro, NJ United States 08028 -
Principal Investigators:
Rasool, Ghulam
Bouaynaya, Nidhal
- Start Date: 20210301
- Expected Completion Date: 20220228
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Algorithms; Artificial intelligence; Helicopters; Heliports; Landing; Location; Machine learning
- Subject Areas: Aviation; Data and Information Technology; Planning and Forecasting;
Filing Info
- Accession Number: 01776216
- Record Type: Research project
- Source Agency: Center for Advanced Infrastructure and Transportation
- Contract Numbers: CAIT-UTC-REG54, 69A3551847102
- Files: UTC, RIP
- Created Date: Jul 7 2021 3:43PM