Description

Data for benchmarking tree species classification from proximally-sensed laser scanning data.

Data split and usage

The data is split into:

• Development data (dev): these includes 90% of the trees in the dataset and consists of individual tree point clouds (*.laz) named according to the treeID column available in the tree_metadata_dev.csv file, from which tree_species labels are available. These data are meant to be used for model development and can thus be further split into training and validation datasets.
• Test data (test): these are 10% of the trees (balanced sample) and include individual tree point clouds (*.laz) but, for benchmarking purposes, the species labels are witheld for benchmarking purposes. Thus to make use of the test data the users should predict species on the test trees, and output a table (.csv file) with a row per predicted tree and two columns (treeID and predicted_species). This table can then be used to create a new submission in the FOR-species20K Codabench benchmarking platform and obtain the evaluation metrics corresponding to the test data.

Cite

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