Benshila R., Thoumyre G., Al Najar M., Abessolo G., Almar Rafaël, Bergsma E., Hugonnard G., Labracherie L., Lavie B., Ragonneau T., Simon E., Vieuble B., Wilson D. (2020). A deep learning approach for estimation of the nearshore bathymetry. Journal of Coastal Research, (No spécial 95), p. 1011-1015. ISSN 0749-0208.
Titre du document
A deep learning approach for estimation of the nearshore bathymetry
Benshila R., Thoumyre G., Al Najar M., Abessolo G., Almar Rafaël, Bergsma E., Hugonnard G., Labracherie L., Lavie B., Ragonneau T., Simon E., Vieuble B., Wilson D.
Source
Journal of Coastal Research, 2020,
(No spécial 95), p. 1011-1015 ISSN 0749-0208
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.
