TreeGPT: Generative Pre-trained Transformer for forestry applications with 3D point clouds

Sun, 10 Aug 2025 00:00:00 +0000

Figure 1: Example of a segmented point cloud of a forest in Canton Neuchâtel used for pre-training.

For my master’s thesis, I tackled a challenge that has captured my interest for years: leveraging LiDAR point clouds to generate automated forest inventories.

I remember when I was studying forest sciences that one of the recurring problems in forest planning was the lack of accurate data about the forest area in question. How to effectively plan the allowable cut, rotation period, tree species composition, and any silvicultural intervention without an accurate overview of what we are working with? Data in this field often comes from the national forest inventory, whose resolution is inadequate when considering individual forest areas. In the past, manual sample inventories were systematically organized by public entities on a widespread basis, but nowadays costs have become prohibitive for efforts of such magnitude.

Pre-print: Automatic inventory of retaining walls from aerial lidar data using 3D deep learning

Sun, 14 Sep 2025 00:00:00 +0000

The paper has been accepted! Post coming soon…

Computer-Vision on Ivo Gasparini

TreeGPT: Generative Pre-trained Transformer for forestry applications with 3D point clouds

Pre-print: Automatic inventory of retaining walls from aerial lidar data using 3D deep learning