3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning

Documentation for package ‘FuelDeep3D’ version 0.1.0

add_ground_csf	Add ground as class 3 using CSF after FuelDeep3D prediction
config	Default config Create model configuration
ensure_py_env	Ensure a Conda environment and Python deps for FuelDeep3D
evaluate_single_las	Evaluate a single LAS using custom field names
evaluate_two_las	Evaluate two LAS files (truth vs prediction)
install_py_deps	Install Python dependencies into a Conda environment
palette_veg	Default vegetation color palette
plot_confusion_matrix	Plot confusion matrix as a heatmap (ggplot2)
plot_las_3d	3D Visualization of LAS Using RGL
predict	Run prediction on cfg$las_path and write output LAS If cfg$num_classes == 4, add ground using CSF automatically.
print_confusion_matrix	Pretty-print confusion matrix
print_metrics_table	Pretty-print evaluation metrics table with summary row
py_setup	Create/use a venv and install Python deps listed in extdata/python/requirements.txt
remove_noise_sor	Remove sparse outlier points using Statistical Outlier Removal (SOR)
train	Train the FuelDeep3D model (auto-preprocess if NPZ tiles are missing)