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.

沿海开发和城市规划面临着不同的问题，包括自然灾害和极端风暴事件。跟踪和预测沿海地区物理特征随时间演变的能力是沿海开发、风险缓解和沿海地区整体管理的重要因素。传统的测深测量是使用回声探测技术获得的，该技术被认为是昂贵的，并且由于各种复杂性而不总是可行。遥感工具（例如卫星图像）可用于使用入射波特征和反演模型（例如波的物理模型）来估计测深。在这项工作中，我们提出了两种使用深度学习进行测深估计的新方法，并在准确性、计算成本、以及对真实数据的适用性。我们表明，深度学习能够在各种模拟情况下准确估计海洋深度，这提供了一种新的测深方法和深度学习的新应用。