GeoTree3D - Synthetic Trees with Aligned Orthophotos, DSMs, and 3D Point Clouds

GeoTree3D is a synthetic dataset for learning-based reconstruction of 3D tree geometry from sparse top-down geospatial data. It consists of procedurally generated trees with aligned RGB orthophotos, Digital Surface Models (DSMs), and colored 3D point clouds. Orthophotos include realistic canopy appearance and shadows under varying illumination, while DSMs encode tree height and crown extent consistent with airborne elevation data. GeoTree3D supports supervised learning and controlled evaluation of tree reconstruction methods from minimal geospatial input.

The dataset is organized into three main folders:

1. DSM, containing .mat files with heightmaps corresponding to individual trees; 

2. ORTHOPHOTOS, containing one folder per tree. Each folder includes a subfolder named rendering, which contains images view_000.png to view_009.png rendered under different illumination conditions, along with a light_directions.txt file specifying the corresponding light directions.

3. TREES, containing .mat files with colored 3D point clouds for each tree and a species_log.txt file mapping tree identifiers to species labels.

All .mat files are binary MATLAB v5 files storing standard numeric arrays (e.g., heightmaps, 3D coordinates, RGB values). They do not require MATLAB and can be opened using common scientific computing tools, such as Python via scipy.io.loadmat.

To facilitate long-term reuse, we additionally provide a loading_data_scripts/ folder containing an example Python script mat_loader.py for loading DSMs, and point clouds, along with a requirements.txt file specifying the Python library versions used. The loading scripts are released under the MIT license.