AI-Driven Identification of Contrail Sources: Integrating Satellite Observation and Air Traffic Data - Archive ouverte HAL
Article Dans Une Revue TU Delft OPEN Publishing Année : 2023

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

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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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hal-04672478 , version 1 (19-08-2024)

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Emmanuel Riggi-Carrolo, Thomas Dubot, Claire Sarrat, Judicaël Bedouet. AI-Driven Identification of Contrail Sources: Integrating Satellite Observation and Air Traffic Data. TU Delft OPEN Publishing, 2023, 1 (2), pp.221-236. ⟨10.59490/joas.2023.7209⟩. ⟨hal-04672478⟩
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