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Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

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Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

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Jiménez-Campfens, N.; Colomer, A.; Núñez, J.; Mogollón, JM.; Rodríguez, AL.; Naranjo Ornedo, V. (2020). Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports. Springer. 148-155. https://doi.org/10.1007/978-3-030-62365-4_14

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/160616

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Title: Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports
Author: Jiménez-Campfens, Néstor Colomer, Adrián Núñez, Javier Mogollón, Juan Manuel Rodríguez, Antonio L. Naranjo Ornedo, Valeriana
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Embargo end date: 2021-10-27
Abstract:
[Otros] New paradigms in aviation, as the expected shortage of qualified pilots and the increasing number of flights worldwide, present big challenges to aeronautic enterprises and regulators. In this sense, a concept known ...[+]
Subjects: Air Traffic Management , Weather reports , METAR , Trajectory prediction , Deep learning
Copyrigths: Embargado
Source:
Intelligent Data Engineering and Automated Learning ¿ IDEAL 2020.
DOI: 10.1007/978-3-030-62365-4_14
Publisher:
Springer
Publisher version: https://doi.org/10.1007/978-3-030-62365-4_14
Conference name: 21st International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2020)
Conference place: Online
Conference date: Noviembre 04-06,2020
Series: Lecture Notes in Computer Science;12490
Project ID:
info:eu-repo/grantAgreement/EC/H2020/831884/EU
Thanks:
This work has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 831884. The Titan V used for this research was donated by the NVIDIA Corporation
Type: Comunicación en congreso

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