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Julian Le Deunf
Julian Le Deunf
Shom
Verified email at shom.fr
Title
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Cited by
Year
A review of data cleaning approaches in a hydrographic framework with a focus on bathymetric multibeam echosounder datasets
J Le Deunf, N Debese, T Schmitt, R Billot
Geosciences 10 (7), 254, 2020
372020
Results from the first phase of the seafloor backscatter processing software inter-comparison project
M Malik, ACG Schimel, G Masetti, M Roche, J Le Deunf, MFJ Dolan, ...
Geosciences 9 (12), 516, 2019
82019
Outlier detection for Multibeam echo sounder (MBES) data: From past to present
D Nathalie, S Thierry, G François, J Etienne, V Lucas, B Romain
OCEANS 2019-Marseille, 1-10, 2019
72019
Automatic Data Quality Assessment of Hydrographic Surveys Taking Into Account Experts’ Preferences
J Le Deunf, A Khannoussi, L Lecornu, P Meyer, J Puentes
OCEANS 2021: San Diego–Porto, 1-10, 2021
32021
Multibeam outlier detection by clustering and topological persistence approach, ToMATo algorithm
M Michel, J Le Deunf, N Debese, L Bazinet, L Dejoie
OCEANS 2021: San Diego–Porto, 1-8, 2021
32021
Automating the Management of 300 Years of Ocean Mapping Effort in Order to Improve the Production of Nautical Cartography and Bathymetric Products: Shom’s Téthys Workflow
J Le Deunf, T Schmitt, Y Keramoal, R Jarno, M Fally
Geomatics 3 (1), 239-249, 2023
22023
A pragmatical algorithm to compute the convex envelope of bathymetric surveys at variable resolutions
J Le Deunf, T Schmitt, Y Keramoal
Abstracts of the ICA 3, 1-2, 2021
12021
Téthys: automating a data workflow compiling over 300 years of bathymetric information
J Le Deunf, R Jarno, Y Keramoal, T Schmitt, M Fally, L Biscara, J Dubuis
OCEANS 2021: San Diego–Porto, 1-6, 2021
12021
Data quality assessment through a preference model
J Le Deunf, A Khannoussi, L Lecornu, P Meyer, J Puentes
ACM Journal of Data and Information Quality 16 (1), 1-21, 2024
2024
Apprentissage automatique de données massives bathymétriques pour l'optimisation de systèmes de levé hydrographique
J Le Deunf
Ecole nationale supérieure Mines-Télécom Atlantique, 2022
2022
Machine learning on massive bathymetric data for the optimization of hydrographic survey systems
J Le Deunf
Ecole nationale supérieure Mines-Télécom Atlantique, 2022
2022
ALGORITHME PRAGMATIQUE POUR CALCULER L’ENVELOPPE DES LEVÉS BATHYMÉTRIQUES À DENSITÉ VARIABLE.
JL Deunf, T Schmitt, Y Keramoal
Cartes et Géomatique, 2022
2022
Seabed prediction from airborne topo-bathymetric lidar point cloud using machine learning approaches
J Le Deunf, R Mishra, Y Pastol, R Billot, S Oudot
OCEANS 2021: San Diego–Porto, 1-9, 2021
2021
Integrating user preferences in the automatic quality assessment of hydrographic surveys
A Khannoussi, J Le Deunf, P Meyer, L Lecornu, J Puentes
The 92nd Meeting of EURO Working Group on Multicriteria Decision Aiding, 2021
2021
Utilisation d'infrastructures géodésiques mondiales pour la réalisation nationale
R Legouge, A Gaël, A Missault, J Le Deunf, S Branchu
Revue XYZ 158, 2019
2019
Exploration du lac de Guerlédan: présentation des projets étudiants en hydrographie et acoustique sous-marine
I Mopin, R Schwab, C Vrignaud, L Berger, J Le Deunf
Congrès Français d’Acoustique, CFA 2018, 2018
2018
Master Thesis Report
J Le Deunf
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Articles 1–17