Mark Crowley | Lidar Pointcloud Processing for Forest Modelling

Lidar Pointcloud Processing for Forest Modelling

Using multi-modal remote sensing data (LiDAR, drones, satellite, etc.) to build generative models of forests.

Using multi-modal remote sensing data (LiDAR, drones, satellite, etc.) to build generative models of forests to improve modelling of fire spread, carbon sequestration, forest management.

Our Papers on Lidar Pointcloud Processing for Forest Modelling

ISPRS

A Generative Upsampling Framework for Reconstructing High-Density Tree Structures from Low-Density Airborne Lidar

Erfan Hasanpour Zaryabi, Liam Bennett, Josh Qixuan Sun, Mark Crowley, Laura Chasmer, Christopher Hopkinson, and Jeff Boisvert.

In XXV ISPRS Congress (Canadian Symposium on Remote Sensing). Toronto, Canada. Jul, 2026.

Abs

Airborne Laser Scanning (ALS) enables efficient large-scale mapping of three-dimensional forest structure due to its extensive spatial coverage, but its low point density often limits the accurate representation of forest structural attributes required for applications such as wildfire management and forest health monitoring. This study proposes a deep generative upsampling framework to enhance low- density ALS tree point clouds and generate dense representations for improved structural analysis. To improve tree reconstruction, the framework incorporates a synthetic pretraining strategy that exposes the model to complete tree geometries before fine-tuning on real data. Experimental results demonstrate that the proposed framework generates high-density tree point clouds with strong spatial similarity to ground-truth point clouds. Synthetic pretraining further improves reconstruction performance, achieving lower Chamfer Distance and higher F1-scores across multiple upsampling rates. In addition, the reconstructed point clouds increase estimation accuracy of key structural attributes, including tree height, crown area, and tree volume. Qualitative experiments on real ALS data confirms the ability of the proposed framework to recover realistic tree structures from sparse point clouds. Overall, the proposed method provides a practical approach for bridging the wide spatial coverage of ALS with the detailed structural information typically obtained from high-density lidar data.