# SimTreeLS: Simulating aerial and terrestrial laser scans of trees

arXiv: https://arxiv.org/abs/2011.11954 · HTML: https://mlsanity.com/paper/2011.11954/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees](https://mlsanity.com/paper/2102.03700/) (2021)
- [Tree Reconstruction using Topology Optimisation](https://mlsanity.com/paper/2205.13192/) (2022)
- [Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches](https://mlsanity.com/paper/2503.05630/) (2025)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)

## Abstract (arXiv)

There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of LiDAR point clouds is increasingly relying on data-driven methods (such as supervised machine learning) that rely on large quantities of hand-labelled data. As this data is potentially expensive to capture, and difficult to clearly visualise and label manually, a means of supplementing real LiDAR scans with simulated data is becoming a necessary step in realising the potential of these methods. We present an open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters. Upon simulation, material classification is kept in a pointwise fashion so leaf and woody matter are perfectly known, and unique identifiers separate individual trees, foregoing post-simulation labelling. This allows for an endless supply of procedurally generated data with similar characteristics to real LiDAR captures, which can then be used for development of data processing techniques or training of machine learning algorithms. To validate our method, we compare the characteristics of a simulated scan with a real scan using similar trees and the same sensor and trajectory parameters. Results suggest the simulated data is significantly more similar to real data than a sample-based control. We also demonstrate application of SimTreeLS on contexts beyond the real data available, simulating scans of new tree shapes, new trajectories and new layouts, with results presenting well. SimTreeLS is available as an open source resource built on publicly available libraries.