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Commits (2)
Title: Deep Learning applications and Coastal waves from optical satellite
Date: 2020-01-15 14:43
Slug: job_8597c13e447a7895338c30757c6be23b
Category: job
Authors: Etienne
Email: etienne.gondet@obs-mip.fr
Job_Type: Stage
Tags: stage
Template: job_offer
Job_Location: Toulouse
Job_Duration: 4 à 6 mois
Job_Website: https://legos.obs-mip.fr
Job_Employer: IRD
Expiration_Date: 2020-02-28
Attachment: job_8597c13e447a7895338c30757c6be23b_attachment.pdf
Laboratoire :UMR5566 LEGOS/OMP https://legos.obs-mip.fr
Contacts : rafael.almar@ird.fr (LEGOS), benshila@legos.obs-mip.fr (LEGOS),
ehouarn.simon@enseeiht.fr (ENSEEIHT), erwin.bergsma@legos.obs-mip.fr (LEGOS), Dennis Wilson (SUPAERO), dennis.wilson@isae.fr
Mission :Coastal regions are currently facing environmental and resource problems aggravated by population pressure and overexploitation. The environmental context or extreme events (floods, coastal erosion) combined with demographic pressure are a limiting factor for coastal development. The general objective of this internship is to improve the representation of sea state and subsequent coastal dynamics at the event scale.
In practice, direct measurements (wave buoys, CANDHIS network) remain costly and difficult. On the other hand, models have been developed and implemented for both coastal circulation and wave representation but are computationally costly and subject to large uncertainties in coastal areas.
The internship will investigate the possibilities to 1) derivate sea states (waves) using optical images from regular basis satellites with global coverage such as Sentinel-2 and 2) replace costly wave models to propagate from offshore deep waters to the coast (incl. extreme sea level/setup).
Using deep learning would represent an efficient way to solve computationally costly wave observation and modelling in coastal zones.
The training will be conducted on a synthetic dataset of more than 12000 numerical simulations of waves with random conditions and the application on Sentinel-2 images.
References :
Perugini, E., Soldini, L., Palmsten, M.L., Calantoni,
J., Brocchini, M., 2019. Linear depth inversion
sensitivity to wave viewing angle using
synthetic optical video, Coastal Engineering,
152, 2019, 103535, ISSN 0378-3839.
Bergsma, E.W.J., Almar, R. and Maisongrande, P.
(2019): Radon-Augmented Sentinel-2 Satellite
Imagery to Derive Wave-Patterns and Regional
Bathymetry, Remote Sensing, vol. 11, p 1918
Profil recherché :Training in deep learning, applied mathematics, experience in image processing geosciences would be a plus, knowledge of a programming language (C, Matlab, Python, Fortran...), knowledge of the
Unix environment.
Date limite de candidature :29 Février 2020
Perspectives : Si stage concluant , possibilité de poursuivre en thèse sur 3 ans.