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

## Documentation for package ‘FuelDeep3D’ version 0.1.0

- DESCRIPTION file.

## Help Pages

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)