Datasets:

Project-AgML
/

nibio_mls_forest_point_cloud_segmentation

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

@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.