---
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., &amp; 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.*