A Deep Learning Approach for Estimation of the Nearshore Bathymetry

被引：16

作者：

Benshila, Rachid ^{[1
]}

Thoumyre, Gregoire ^{[1
]}

Al Najar, Mahmoud ^{[1
]}

Abessolo, Gregoire ^{[1
]}

Almar, Rafael ^{[1
]}

Bergsma, Erwin ^{[1
]}

Hugonnard, Guillaume ^{[2
]}

Labracherie, Laurent ^{[4
]}

Lavie, Benjamin ^{[2
]}

Ragonneau, Tom ^{[2
]}

Simon, Ehouarn ^{[2
]}

Vieuble, Bastien ^{[2
]}

Wilson, Dennis ^{[3
]}

机构：

[1] Univ Paul Sabatier, Lab Etudes Geophys & Oceanog Spatiales, Toulouse, France

[2] Inst Natl Polytech Toulouse, Toulouse, France

[3] Inst Super Aeronault & Espace, Toulouse, France

[4] ALTRAN, Toulouse, France

来源：

JOURNAL OF COASTAL RESEARCH | 2020年

关键词：

Bathymetry; deep Learning; Big Data; morphodynamics;

D O I：

10.2112/SI95-197.1

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：1011 / 1015

页数：5

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