Satellite derived bathymetry using deep learning

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