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            <title xml:lang="en">AI-Driven Identification of Contrail Sources: Integrating Satellite Observations and Air Traffic Data</title>
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                <forename type="first">Emmanuel</forename>
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                <title xml:lang="en">AI-Driven Identification of Contrail Sources: Integrating Satellite Observations and Air Traffic Data</title>
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                <term xml:lang="en">Contrails</term>
                <term xml:lang="en">Detection</term>
                <term xml:lang="en">Deep learning</term>
                <term xml:lang="en">Air traffic</term>
                <term xml:lang="en">Artificial intelligence</term>
                <term xml:lang="en">Climate</term>
                <term xml:lang="en">Non-CO2 effects</term>
                <term xml:lang="fr">Traînées de condensation</term>
                <term xml:lang="fr">Détection</term>
                <term xml:lang="fr">Deep learning</term>
                <term xml:lang="fr">Trafic aérien</term>
                <term xml:lang="fr">Intelligence artificielle</term>
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                <term xml:lang="fr">Effet non-CO2</term>
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              <p>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.</p>
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