add_ground_csf          Add a ground class using CSF post-processing
config                  Create a FuelDeep3D configuration
ensure_py_env           Ensure a Conda environment and Python
                        dependencies for FuelDeep3D
evaluate_single_las     Evaluate predictions stored in a single LAS/LAZ
evaluate_two_las        Evaluate predictions stored in two LAS/LAZ
                        objects
install_py_deps         Install Python dependencies into a Conda
                        environment (FuelDeep3D)
las_class_distribution
                        Class distribution summary for a LAS point
                        cloud
plot_3d                 Plot a 3D LAS point cloud colored by elevation
plot_confusion_matrix   Plot a confusion matrix heatmap (ggplot2)
predict                 Predict fuel classes for a LAS/LAZ file using a
                        pre-trained model
predicted_plot3d        Plot a LAS point cloud in 3D colored by a class
                        field
print_confusion_matrix
                        Print a confusion matrix (LAS/LAZ or
                        precomputed cm)
print_metrics_table     Print per-class metrics and summary averages
remove_noise_sor        Remove sparse outlier points using Statistical
                        Outlier Removal (SOR)
train                   Train the FuelDeep3D model (build NPZ tiles if
                        missing)
