Datasets:

Project-AgML
/

lautx_tree_segmentation

dataset_info:
  features:
    - name: tile
      dtype: string
    - name: plot
      dtype: string
    - name: points
      dtype:
        array2_d:
          shape:
            - null
            - 3
          dtype: float32
    - name: intensity
      dtype:
        array2_d:
          shape:
            - null
            - 1
          dtype: uint16
    - name: mask
      dtype:
        array2_d:
          shape:
            - null
            - 1
          dtype: int32
    - name: mask_2
      dtype:
        array2_d:
          shape:
            - null
            - 1
          dtype: int32
    - name: mask_3
      dtype:
        array2_d:
          shape:
            - null
            - 1
          dtype: int32
    - name: num_points
      dtype: int64
    - name: num_trees
      dtype: int32
  splits:
    - name: train
      num_bytes: 14098751862
      num_examples: 54
  download_size: 2790962917
  dataset_size: 14098751862
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-segmentation
size_categories:
  - n<1K

Lautx Tree Segmentation

LAUTx: personal laser scanning (PLS) point clouds of six forest plots in Austria, used as a test set for SegmentAnyTree, a sensor- and platform-agnostic deep learning model for individual tree segmentation in laser scanning data. Each plot (33-85 million points) is split into a 3x3 grid of 14 m tiles, one row per tile (local coordinates in metres), with intensity, a per-point semantic label (ground, unlabeled, tree) and two versions of per-point tree instance ids: the updated TreeLearn labels (mask_2) and the original manual labels (mask_3). The dataset contains 54 images with pixel-level mask annotations.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{wielgosz2024segmentanytree,
  title={SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data},
  author={Wielgosz, Maciej and Puliti, Stefano and Xiang, Binbin and Schindler, Konrad and Astrup, Rasmus},
  journal={Remote Sensing of Environment},
  volume={313},
  pages={114367},
  year={2024},
  publisher={Elsevier}
}

The dataset itself can be cited as:

Tockner Andreas, Burmeister, J.-M., Gollob Christoph, Ritter Tim, & Nothdurft Arne. (2025). LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots (Version v2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.16910335

This dataset was reformatted from its original format to match HuggingFace standards.