AI-Driven Identification of Contrail Sources: Integrating Satellite Observations and Air Traffic Data
Résumé
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 observationsneeds to be built up to improve prediction models and to enable aircraft trajectories optimization basedon 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 onsegmentation methods. Then, a method is introduced for associating contrails with aircraft trajectoriesbased on ADS-B data. The Hough transform and meteorological forecast reanalysis data are applied tolink any contrail with a group of flights that may have contributed to its formation.
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