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

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.