Machine Learning Strategies for Enhancing Bathymetry Extraction from Imbalanced Lidar Point Clouds

Density-based approaches to extract bathymetry from airborne lidar point clouds generally rely on histogram/frequency-based disambiguation rules to separate noise from signal. The present work targets the improvement of such disambiguation rules by enhancing each pulse with a machine learning-based estimate of its p(Bathy) - i.e., its probability of truly being bathymetry. Extreme gradient boosting (XGB) is used to assess the strength of...