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. Bathymetry deep Learning Big Data morphodynamics 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. 2020 text https://www.documentation.ird.fr/hor/fdi:010078156 fdi:010078156 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.. A deep learning approach for estimation of the nearshore bathymetry. 2020, 1011-1015 EN