Study of various machine learning approaches for Sentinel-2 derived bathymetry.

ABSTRACT: In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most c

SUBMITTER: Chybicki A 

PROVIDER: S-EPMC10503737 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature