DetailView model for tree species classification based on single tree LiDAR data

Tree species classification for 3D4EcoTec by Zoe Schindler and Julian Frey (Chair of Forest Growth and Dendroecology, University of Freiburg)

The project is based on pytorch. Point clouds of single trees are augmented and projected to grids (4 side views, 1 top view, 1 bottom view, 1 detail view 1 - 1.5 m height).
The dataset is available on Zenodo.

The training weights are available on FreiData.

Classification is done with three densenet201 instances, one for the side views, one for the top and bottom views and the last one for the detail view. These classifications are merged and the tree height is also added. After that, two more linear layers with a relu layer in between are used as classifiers.

The final predictions are made by adding the probabilities of 50 augmentations of each test tree and selecting the most probable class.

Any scientific publication using the data should cite the following paper:

Puliti, S., Lines, E., Müllerová, J., Frey, J., Schindler, Z., Straker, A., Allen, M.J., Winiwarter, L., Rehush, N., Hristova, H., Murray, B., Calders, K., Terryn, L., Coops, N., Höfle, B., Krůček, M., Krokm, G., Král, K., Luck, L., Levick, S.R., Missarov, A., Mokroš, M., Owen, H., Stereńczak, K., Pitkänen, T.P., Puletti, N., Saarinen, N., Hopkinson, C., Torresan, C., Tomelleri, E., Weiser, H., Junttila, S., and Astrup, R. (2024) Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset. ArXiv; available here