Coastal development and urban planning are facing different issues including natural disasters and extreme storm events. The ability to track and forecast the evolution of the physical characteristics of coastal areas over time is an important factor in coastal development, risk mitigation and overall coastal zone management. Traditional bathymetry measurements are obtained using echo-sounding techniques which are considered expensive and not always possible due to various complexities. Remote sensing tools such as satellite imagery can be used to estimate bathymetry using incident wave signatures and inversion models such as physical models of waves. In this work, we present two novel approaches to bathymetry estimation using deep learning and we compare the two proposed methods in terms of accuracy, computational costs, and applicability to real data. We show that deep learning is capable of accurately estimating ocean depth in a variety of simulated cases which offers a new approach for bathymetry estimation and a novel application for deep learning.

The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.

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