This repository contains code used to train the Deep Neural Network (DNN) and Random Forest (RF) models from the study "Modelling Tree Biomass Using Direct and Additive Methods with Point Cloud Deep Learning in a Temperate Mixed Forest" by Seely et al. 2023. This repository should be used as a reference for information relating to the specific model architectures, training regimes, or any other technical details of the analysis.
Please note that this repository is not set up as a python package. This code is provided t0 serve as a useful reference for further research and applications of point cloud deep learning for tree biomass regression.
Seely, H., Coops, N.C., White, J.C., Montwé, D., Winiwarter, L., Ragab, A., 2023. Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest. Science of Remote Sensing 100110. https://doi.org/10.1016/j.srs.2023.100110
The following GitHub repos were essential in the development of the code used in this study: