General description This dataset consists of a ML-ready labelled mobile laser scanning (MLS) point cloud dataset including 16 manually labelled forest plots (approx. 250 m2) for forest panoptic segmentation and thus including both semantic and instance labels. The data was collected using a Geoslam Horizon RT and processed using Geoslam Hub. Labels The data were then labelled into the following semantic classes (label): 1= ground 2= vegetation: these include both branches, leaves, and low vegetation 3= lying deadwood 4= stems In addition for each tree, a unique tree identifier (treeID) was also assigned to each point. Data split Each plot was split into train (50%), validation (25%), and test (25%) sets by dividing the circular plot into four slices, out of which the first two were used for training, the third for validation, and the fourth for test. Thus the users might play around with merging the train and validation dataset as they prefer. These two sets can be used during model training, hyperparameter tuning, and model selection. However, the test set should be kept as an independent set to be used for benchmarking against the values reported in the two studies indicated below. Citation To cite this datasets and for a more detailed description use: Wielgosz, M., Puliti, S., Xiang, B., Schindler, K. and Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sensing of Environment; Other studies using these data Wielgosz, M., Puliti, S., Wilkes, P. and Astrup, R., 2023. Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest. Remote Sensing, 15(15), p.3737; available here Funding This work is part of the Center for Research-based Innovation SmartForest: Bringing Industry 4.0 tothe Norwegian forest sector (NFR SFI project no. 309671, smartforest.no).