The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks: Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spati
Authors
Pierre Maréchal (ORCID: https://orcid.org/0000-0001-9148-831X)
I. Berry (ORCID: https://orcid.org/0000-0002-0886-3252)
Daniel Delahaye (ORCID: https://orcid.org/0000-0002-4965-6815)
Adrien Marque
Institutions
École Nationale de l’Aviation Civile (FR)
Université Toulouse III - Paul Sabatier (FR)
Institut National des Sciences Appliquées de Toulouse (FR)
Centre de recherche cerveau et cognition (FR)
Institut de Mathématiques de Toulouse (FR)
Publication Details
Journal
SPIRE - Sciences Po Institutional REpository
Published
2024-10-01
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
Funders
Centre National de la Recherche Scientifique
Siemens Healthineers
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article
The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks: Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spati
Pierre Maréchal, I. Berry, Daniel Delahaye, Adrien Marque
Journal
SPIRE - Sciences Po Institutional REpository
Topics
Autonomous Vehicle Technology and Safety
article
The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks: Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spati
Pierre Maréchal, I. Berry, Daniel Delahaye, Adrien Marque
article en
Abstract
No abstract available for this paper.
Publication Tag
SPIRE - Sciences Po Institutional REpository
Institutions
École Nationale de l’Aviation Civile (FR), Université Toulouse III - Paul Sabatier (FR), Institut National des Sciences Appliquées de Toulouse (FR), Centre de recherche cerveau et cognition (FR), Institut de Mathématiques de Toulouse (FR)
Funders
Centre National de la Recherche Scientifique, Siemens Healthineers
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The UAV Feasibility Trajectory Prediction Using Convolution Neural Networks: Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spati — Pierre Maréchal, I. Berry, et al. · SPIRE - Sciences Po Institutional REpository (2024) | TGRS Research Map | TGRS