TY - GEN
T1 - A Deep Learning Approach for Airport Runway Detection and Localization from Satellite Imagery
AU - Khelifi, Amine
AU - Gemici, Mahmut
AU - Carannante, Giuseppina
AU - Johnson, Charles C.
AU - Bouaynaya, Nidhal C.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 However, outdated or inaccurate information poses risks to aviation safety.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%.
AB - 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 However, outdated or inaccurate information poses risks to aviation safety.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%.
UR - https://www.scopus.com/pages/publications/85172007180
UR - https://www.scopus.com/inward/citedby.url?scp=85172007180&partnerID=8YFLogxK
U2 - 10.1109/ISCC58397.2023.10217868
DO - 10.1109/ISCC58397.2023.10217868
M3 - Conference contribution
AN - SCOPUS:85172007180
T3 - Proceedings - IEEE Symposium on Computers and Communications
SP - 1066
EP - 1069
BT - ISCC 2023 - 28th IEEE Symposium on Computers and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE Symposium on Computers and Communications, ISCC 2023
Y2 - 9 July 2023 through 12 July 2023
ER -