ForestSemantic-MS Dataset: multispectral LiDAR forest point clouds for fine-grained forest semantic segmentation

Description ForestSemantic-MS dataset contains multispectral (MS) LiDAR forest point clouds used in our paper titled 3D Forest Semantic Segmentation Using Multispectral LiDAR and 3D Deep Learning (DOI: 10.1007/s41064-025-00369-4). This dataset is collected by the helicopter-mounted HeliALS multispectral LiDAR system developed by the Finnish Geospatial Research Institute (FGI). The MS point clouds are manually annotated in six forest components: ground, low vegetation, trunk, branches, foliage, and woody debris. ForestSemantic-MS consists of six forest plots used to train (four plots) and evaluate (two plots) deep learning models we benchmarked in our paper. Point cloud attributes Attribute Description SWIR Normalized ([0-1]) reflectance at 1550 nm NIR Normalized ([0-1]) reflectance at 905 nm Green Normalized ([0-1]) reflectance at 532 nm VI Normalized ([0-1]) vegetation index (NDVI NIR-SWIR ) semantic_GT Semantic segmentation labels Labels Class Name 0 Ground 1 Low vegetation 2 Trunks 3 Branches 4 Foliage 5 Woody debris ForestSemantic-MS vs. Other Public Datasets FOR-Instance (Xiang et al., 2024) ForestSemantic (Liang et al., 2024) EvoMS (Ruoppa et al., 2025) ForestSemantic-MS (ours) Forest component detail level 5 (Ground, low vegetation, stem, live branches, and dead branches) 3 (Trunk, branches, and foliage) 2 (Foliage and wood) 6 (Ground, low vegetation, trunk, branches, foliage, and woody debris) Multispectral LiDAR ✕ ✕ ✓ (1550 nm, 905 nm, and 532 nm) ✓ (1550 nm, 905 nm, and 532 nm) Vegetation index ✕ ✕ ✕ NDVI NIR-SWIR Platform ULS TLS ULS ULS Forest type(s) Boreal, temperate, alluvial, and eucalypt forests Boreal forests Boreal forests Boreal forests Geographical data coverage Norway, Austria, the Czech Republic, Australia, and New Zealand Finland Finland Finland Number of points 116,099,253 355,511,770 8,265,448 9,600,927 Citation Any scientific publication using this dataset should cite the following paper and the dataset: Takhtkeshha, N., Bocaux, L., Ruoppa, L., Remondino, F., Mandlburger, G., Kukko, A., & Hyyppä, J. (2025). 3D forest semantic segmentation using multispectral LiDAR and 3D deep learning. PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science. https://doi.org/10.1007/s41064-025-00369-4 Takhtkeshha, N., Bocaux, L., Ruoppa, L., Remondino, F., Mandlburger, G., Kukko, A., Hyyppä, J. ForestSemantic-MS Dataset:735 multispectral LiDAR forest point clouds for fine-grained forest semantic segmentation [dataset], 2025a. doi:736 10.5281/zenodo.17172162