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3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow.
Estimation of Physical Parameters of Waveforms With Neural Networks
By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance.
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