1. A Deep Learning Approach for Estimation of the Nearshore Bathymetry.
- Author
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Benshila, Rachid, Thoumyre, Grégoire, Najar, Mahmoud Al, Abessolo, Grégoire, Almar, Rafael, Bergsma, Erwin, Hugonnard, Guillaume, Labracherie, Laurent, Lavie, Benjamin, Ragonneau, Tom, Simon, Ehouarn, Vieuble, Bastien, and Wilson, Dennis
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DEEP learning , *BATHYMETRY , *REMOTE-sensing images , *BIG data , *SURFACE area , *ADVENT - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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