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Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches

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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.
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Dates and versions

hal-03583749 , version 1 (22-02-2022)

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Julian Le Deunf, Rudresh Mishra, Yves Pastol, Romain Billot, Steve Oudot. Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches. OCEANS 2021: San Diego – Porto, Sep 2021, San Diego, United States. pp.1-9, ⟨10.23919/OCEANS44145.2021.9706113⟩. ⟨hal-03583749⟩
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