Tree Instance Propagation to Original Plot Point Cloud

This repo contains two scripts aimed at removing points present in tree instances from original plot point clouds using distance-based thresholding.
Given a set of segmented tree point clouds, and the original, unedited plot point cloud, the segmented tree points will be propagated to the plot point cloud and removed.

---

📄 treeseg_propagation.py

This script creates a propagated point cloud (i.e., the original plot point cloud without the segmented trees).

How to run: Replace the paths in the main function at the bottom of the script and execute the Python file.

Dependencies:

• open3d
• laspy
  (Tested using Ubuntu + Python 3.10)

Notes:

• The trees must be in the original coordinate system.
• With minor adaptations, the same code can be used to label (instead of remove) points — e.g., for instance segmentation training/testing data.
• The script reads .las files. To use .ply or .txt, write a custom reader function with the same output format as read_las_np, and adjust other functions accordingly.
• The algorithm is split into two steps due to potential memory issues, especially with undownsampled data.
  • If the function fails in the second step, a plot mask is saved as an .npy file.
  • You can load the saved mask in the main function to avoid recomputing.

Contact:
📧 wout.cherlet@ugent.be

---

📄 tree_extraction.py

This script creates both:

1. A propagated point cloud
2. Individual tree point clouds using points from the original plot point cloud.

How to run:
Execute from the command line:

python tree_extraction_v2.py <input_file.las> <input_directory> <output_directory> <propagated_file.las> --distance_th <value>

Example:

python tree_extraction_v2.py /path/to/input.las /path/to/input_dir/ /path/to/output_dir/ /path/to/propagated.las --distance_th 0.015

Dependencies:

• open3d
• laspy
• laz backends
  • lazrs or,
  • laszip
• argparse

Notes:

• The trees must be in the original coordinate system.
• The script reads .las and .laz files automatically. It recognizes if you have a laz backend installed.
• As with the other script, the algorithm is split into two steps due to memory constraints.
  • If the second step fails, the plot mask is saved as an .npy file and can be reloaded later.

Contact:
📧 geike.desloover@ugent.be
📧 louise.terryn@ugent.be