AI-Driven Identification of Contrail Sources: Integrating Satellite Observation and Air Traffic Data

Authors

  • Emmanuel Riggi--Carrolo DGAC
  • Thomas Dubot ONERA/DTIS
  • Claire Sarrat ONERA/DTIS
  • Judicaël Bedouet ONERA/DTIS

DOI:

https://doi.org/10.59490/joas.2023.7209

Keywords:

Contrails, Deep Learning, ADS-B

Abstract

Despite large uncertainties, it is now clear that condensation trails play a major role in aviation contribu-
tion to climate change. In order to assess these uncertainties and reduce them, a database of observations
needs to be built up to improve prediction models and to enable aircraft trajectories optimization based
on climate considerations. Detecting contrails in images is a time-consuming task without automation.
In this paper, a dataset from GOES-16 satellite images is used to create a detection algorithm based on
segmentation methods. Then, a method is introduced for associating contrails with aircraft trajectories
based on ADS-B data. The Hough transform and meteorological forecast reanalysis data are applied to
link any contrail with a group of flights that may have contributed to its formation.

Published

2023-10-30

How to Cite

Riggi--Carrolo, E., Dubot, T. ., Sarrat, C., & Bedouet, J. (2023). AI-Driven Identification of Contrail Sources: Integrating Satellite Observation and Air Traffic Data. Journal of Open Aviation Science, 1(2). https://doi.org/10.59490/joas.2023.7209