---
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

```bibtex
@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, &amp; 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.*