| --- |
| dataset_info: |
| features: |
| - name: plant_id |
| dtype: string |
| - name: points |
| dtype: |
| array2_d: |
| shape: |
| - -1 |
| - 3 |
| dtype: float32 |
| - name: mask |
| dtype: |
| array2_d: |
| shape: |
| - -1 |
| - 1 |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 1040095958 |
| num_examples: 59 |
| download_size: 726180357 |
| dataset_size: 1040095958 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-sa-4.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| # Tls Treevolume |
|
|
| This dataset provides real-world point cloud data collected via terrestrial laser scanning in forested environments, capturing detailed 3D representations of tree structures. It is annotated for semantic segmentation to identify and label tree components, enabling volumetric analysis and forest inventory applications. The high-resolution 3D point clouds offer rich spatial information for forestry and ecological research. The dataset contains 59 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{bornand2023individual, |
| title={Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches}, |
| author={Bornand, Aline and Rehush, Nataliia and Morsdorf, Felix and Th{\"u}rig, Esther and Abegg, Meinrad}, |
| journal={Agricultural and forest meteorology}, |
| volume={341}, |
| pages={109654}, |
| year={2023}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Bornand, A. (2023). Individual tree TLS point clouds for tree volume estimation. EnviDat. https://www.doi.org/10.16904/envidat.403. |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
