| --- |
| 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, & 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.* |
|
|
