| --- |
| 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: num_points |
| dtype: int64 |
| - name: split |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 5627502122 |
| num_examples: 64 |
| download_size: 1007346819 |
| dataset_size: 5627502122 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: agpl-3.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| # Nibio Mls Forest Point Cloud Segmentation |
|
|
| NIBIO_MLS: 64 mobile laser scanning (GeoSLAM ZEB-HORIZON) tiles from 16 plots of managed, coniferous-dominated boreal forest in south-eastern Norway, used as one of the real-world benchmarks in a study of multi-platform training for forest point cloud segmentation. Each row is one tile's point cloud (local coordinates in metres) with intensity, a per-point semantic label (ground, vegetation, lying deadwood, stem) and a per-point tree instance id (mask_2), in the published train, validation and test splits. The dataset contains 64 images with pixel-level mask annotations. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| The original train/test/val split has been preserved in the `split` column. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{jiang2026cross, |
| title={Cross-platform forest understanding: A multi-platform synergistic training framework for generalized forest point cloud segmentation}, |
| author={Jiang, Jundi and Shen, Yueqian and Wang, Jinhu and Kissling, W. Daniel and Hollaus, Markus and Su, Hongjun and Wang, Jinguo and Ferreira, Vagner and Pfeifer, Norbert}, |
| journal={Remote Sensing of Environment}, |
| volume={342}, |
| pages={115467}, |
| year={2026}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
|
|
| The dataset itself can be cited as: |
|
|
| Puliti, S., & Astrup, R. (2024). *NIBIO_MLS: a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.12754726 |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
