R: 3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning

# 3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning

## Documentation for package ‘FuelDeep3D’ version 0.1.1

- DESCRIPTION file
- Package README

## Help Pages

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)