A Deep Learning Approach for Airport Runway Detection and Localization from Satellite Imagery

Amine Khelifi, Mahmut Gemici, Giuseppina Carannante, Charles C. Johnson, Nidhal C. Bouaynaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The US lacks a complete national database of private prior permission required airports due to insufficient federal requirements for regular updates. The initial data entry into the system is usually not refreshed by the Federal Aviation Administration (FAA) or local state Department of Transportation. However, outdated or inaccurate information poses risks to aviation safety. This paper suggests a deep learning (DL) approach using Google Earth satellite imagery to identify and locate airport landing sites. The study aims to demonstrate the potential of DL algorithms in processing satellite imagery and improve the precision of the FAA's runway database. We evaluate the performance of Faster Region-based Convolutional Neural Networks using advanced backbone architectures, namely Resnet101 and Resnet-X152, in the detection of airport runways. We incorporate negative samples, i.e., highways images, to enhance the performance of the model. Our simulations reveal that Resnet-X152 outperformed Resnet101 achieving a mean average precision of 76%.

Original languageEnglish (US)
Title of host publicationISCC 2023 - 28th IEEE Symposium on Computers and Communications
Subtitle of host publicationComputers and Communications for the Benefits of Humanity
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1066-1069
Number of pages4
ISBN (Electronic)9798350300482
DOIs
StatePublished - 2023
Externally publishedYes
Event28th IEEE Symposium on Computers and Communications, ISCC 2023 - Hybrid, Gammarth, Tunisia
Duration: Jul 9 2023Jul 12 2023

Publication series

NameProceedings - IEEE Symposium on Computers and Communications
Volume2023-July
ISSN (Print)1530-1346

Conference

Conference28th IEEE Symposium on Computers and Communications, ISCC 2023
Country/TerritoryTunisia
CityHybrid, Gammarth
Period7/9/237/12/23

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • General Mathematics
  • Computer Science Applications
  • Computer Networks and Communications

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