Rotorcraft Landing Sites – An AI-Based Identification System

The updated information about the location and type of landing sites is an essential asset for the Federal Aviation Administration (FAA) and the Department of Transportation (DOT). However, the acquisition, verification, and regular updating of information about landing sites is not an easy or straightforward task, and the lack of current and correct information on helicopter landing sites is a risk factor in several accidents and incidents involving rotorcraft. The primary goal of this proposal is to create an artificial intelligence (AI)-based system for the identification of helipads, heliports, and landing site infrastructure from various heterogeneous datasets, including video from rotorcraft, drones, satellite images, or aerial imagery, as well as textual data sources (i.e., data entered by helipad owners/operators or pilots) from other sources. The intended outcome of the project is to generate an AI algorithm that will automate the process of identification of landing sites from video data as well as satellite images. The researchers hope 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 FAA to update its databases of landing sites regularly without any delays so the information could be used by any mission, including “Helicopter Air Ambulance missions to rural communities.”

Language

  • English

Project

  • Status: Active
  • Funding: $160000
  • Contract Numbers:

    69A3551847102

    CAIT-UTC-REG 32

  • 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:

    Federal Aviation Administration

    William J. Hughes Technical Center, Atlantic City International Airport
    Atlantic City, NJ  United States  08405

    New Jersey Department of Transportation

    1035 Parkway Avenue
    Trenton, NJ  United States  08625

    Center for Advanced Infrastructure and Transportation

    Rutgers University
    100 Brett Road
    Piscataway, NJ  United States  08854-8058
  • Project Managers:

    Johnson, Charles (Cliff)

    Stott, Glenn

    Szary, Patrick

  • Performing Organizations:

    Rowan University

    College of Engineering
    201 Mullica Hill Road
    Glassboro, NJ  United States  08028
  • Principal Investigators:

    Rasool, Ghulam

    Bouaynaya, Nidhal

    Jalayer, Mohammad

  • Start Date: 20200101
  • Expected Completion Date: 20201231
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01727612
  • Record Type: Research project
  • Source Agency: Center for Advanced Infrastructure and Transportation
  • Contract Numbers: 69A3551847102, CAIT-UTC-REG 32
  • Files: UTC, RiP
  • Created Date: Jan 17 2020 11:06AM