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.”


  • English


  • Status: Completed
  • Funding: $120846
  • 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:

    Federal Aviation Administration Rotorcraft, Unmanned Aircraft Systems (UAS), and eVTOL/Urban Air Mobility System Safety Section, ANG-E272 Aviation Research Division NextGen WJHTC Office

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

    Center for Advanced Infrastructure and Transportation

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

    Johnson, Charles (Cliff)

    Szary, Patrick

  • 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

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 4:59PM