@inproceedings{f98e8f14dae34623b3e7f2850c1b44c5,
title = "Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches",
abstract = "Predicting the seabed from unfiltered bathymetric lidar data is a very complex task and a critical issue in bathymetric data processing especially with the objective of nautical charting. This is challenging to ensure a high level of quality and security for the needs of a national hydrographic office. This paper proposes a methodology to predict the seabed based on machine learning, which could be useful to automate outlier detection and control the topo-bathymetric lidar point cloud datasets. Several predictive methods have been investigated to predict the seabed from our 2D + 1D data structure. A characteristic dataset of Corsica region was used as a case study for this predictive workflow.",
keywords = "Data processing, Supervised machine learning methods, Topo-bathymetric lidar data",
author = "Deunf, \{Julian Le\} and Rudresh Mishra and Yves Pastol and Romain Billot and Steve Oudot",
note = "Publisher Copyright: {\textcopyright} 2021 MTS.; OCEANS 2021: San Diego - Porto ; Conference date: 20-09-2021 Through 23-09-2021",
year = "2021",
month = jan,
day = "1",
doi = "10.23919/OCEANS44145.2021.9706113",
language = "English",
series = "Oceans Conference Record (IEEE)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "OCEANS 2021",
}