A new look to an old problem: using Gaussian Mixture Models and topo-bathymetric LiDAR data for morphological classification of large lowland rivers

Alex Andréault; Stéphane Rodrigues; Corentin Gaudichet

doi:https://doi.org/10.5194/icg2022-709

[Back] [Session ICG2022-10]

ICG2022-709

https://doi.org/10.5194/icg2022-709

10th International Conference on Geomorphology

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

A new look to an old problem: using Gaussian Mixture Models and topo-bathymetric LiDAR data for morphological classification of large lowland rivers

Alex Andréault¹, Stéphane Rodrigues², and Corentin Gaudichet³

Alex Andréault et al.

¹UMR CITERES, Université de Tours, Tours, France (andreault@univ-tours.fr)
²UMR CITERES, Université de Tours, Tours, France (srodrigues@univ-tours.fr)
³UMR CITERES, Université de Tours, Tours, France (gaudichet@univ-tours.fr)

How to cite: Andréault, A., Rodrigues, S., and Gaudichet, C.: A new look to an old problem: using Gaussian Mixture Models and topo-bathymetric LiDAR data for morphological classification of large lowland rivers, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-709, https://doi.org/10.5194/icg2022-709, 2022.

This abstract has been withdrawn on 26 Aug 2022.