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Conference papers

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

Julian Le Deunf 1, 2, 3 Rudresh Mishra 4 Yves Pastol 1 Romain Billot 2, 3 Steve Oudot 4
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
4 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
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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Submitted on : Tuesday, February 22, 2022 - 9:17:24 AM
Last modification on : Monday, April 4, 2022 - 9:28:32 AM
Long-term archiving on: : Monday, May 23, 2022 - 6:15:36 PM


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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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