Benshila, R.; Thoumyre, G.; Al Najar, M.; Absessolo, G.; Almar, R.; Bergsma, E.; Hugonnard, G.; Labracherie, L.; Lavie, B.; Ragonneau, T.; Simon, E.; Vieuble, B., and Wilson D., 2020. A deep learning approach for estimation of the nearshore bathymetry. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1011-1015. Coconut Creek (Florida), 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.

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