%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Benshila, R.
%A Thoumyre, G.
%A Al Najar, M.
%A Abessolo, G.
%A Almar, Rafaël
%A Bergsma, E.
%A Hugonnard, G.
%A Labracherie, L.
%A Lavie, B.
%A Ragonneau, T.
%A Simon, E.
%A Vieuble, B.
%A Wilson, D.
%T A deep learning approach for estimation of the nearshore bathymetry
%D 2020
%L fdi:010078156
%G ENG
%J Journal of Coastal Research
%@ 0749-0208
%K Bathymetry ; deep Learning ; Big Data ; morphodynamics
%M ISI:000537556600189
%N No spécial 95
%P 1011-1015
%R 10.2112/si95-197.1
%U https://www.documentation.ird.fr/hor/fdi:010078156
%> https://www.documentation.ird.fr/intranet/publi/2020/06/010078156.pdf
%W Horizon (IRD)
%X Bathymetry is an important factor in determining wave and current transformation in coastal and surface areas but is often poorly understood. However, its knowledge is crucial for hydro-morphodynamic forecasting and monitoring. Available for a long time only via in-situ measurement, the advent of video and satellite imagery has allowed the emergence of inversion methods from surface observations. With the advent of methods and architectures adapted to big data, a treatment via a deep learning approach seems now promising. This article provides a first overview of such possibilities with synthetic cases and its potential application on a real case.
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