Mangrove3D: Terrestrial Laser Scanning Dataset for Coastal Mangrove Forests

Mangrove3D is a terrestrial laser scanning (TLS) dataset for 3D point cloud semantic segmentation, collected in spring 2024 on Babeldaob Island, Palau (7°31′49″N, 134°33′53″E). The study sites consist of coastal Rhizophora mangrove stands characterized by dense prop-root systems and multilayered canopies.

Data were acquired using a Canopy Biomass LiDAR v2.0 (CBL) system based on a SICK LMS151 scanner (905 nm), mounted on a rotating stage and inverted on a surface elevation table (SET) arm to maximize near-ground and root sampling. Each scan covers a 360° × 270° field of view at 0.25° angular resolution and is completed in approximately 33 s. At each benchmark site, eight scans were collected at 45° azimuthal intervals; scans exhibiting obvious acquisition errors were excluded.

The dataset contains 39 scans from 7 benchmark sites, comprising approximately 31.3 million points (0.5–0.9 M points per scan). Points are annotated into six semantic classes: Ground & Water, Stem, Canopy, Root, Object, and Void. For reproducible benchmarking, we recommend using 30 scans (sites #1–5) for training and validation and 9 scans (sites #6–7) for testing.

Mangrove3D is among the first open TLS datasets specifically targeting mangrove ecosystems. It provides high-density 3D geometry suitable for semantic segmentation, structural analysis, biomass estimation, and coastal blue-carbon research.

Dataset Highlights

• High-resolution TLS dataset dedicated to body-level semantic segmentation of mangrove forests, including explicit annotation of prop-root structures.

• One of the first open-access 3D point cloud datasets capturing coastal Rhizophora mangrove stands.

• Points annotated into 5 classes: Ground&Water, stem, canopy, root, and object.

• Spherical projection maps with corresponding ground-truth semantic segmentation masks provided.

We also have a project web page for this dataset: https://fz-rit.github.io/through-the-lidars-eye/

If you use this dataset in your research, please consider citing our work:

• @dataset{zhang_2026_16933584,
    author       = {Zhang, Fei and
                    Chancia, Robert and
                    Clapp, Josie and
                    Hassanzadeh, Amirhossein and
                    Dera, Dimah and
                    MacKenzie, Richard and
                    van Aardt, Jan},
    title        = {Mangrove3D: Terrestrial Laser Scanning Dataset for
                     Coastal Mangrove Forests
                    },
    month        = feb,
    year         = 2026,
    publisher    = {Zenodo},
    doi          = {10.5281/zenodo.16933584},
    url          = {https://doi.org/10.5281/zenodo.16933584},
  }
• @article{ZHANG2026141,

                title = {Through the perspective of LiDAR: A feature-enriched and uncertainty-aware annotation pipeline for terrestrial point cloud segmentation},

                journal = {ISPRS Journal of Photogrammetry and Remote Sensing},

                volume = {236},

                pages = {141-161},

                year = {2026},

                issn = {0924-2716},

                doi = {https://doi.org/10.1016/j.isprsjprs.2026.03.033},

                url = {https://www.sciencedirect.com/science/article/pii/S0924271626001474},

                author = {Fei Zhang and Rob Chancia and Josie Clapp and Amirhossein Hassanzadeh and Dimah Dera and Richard MacKenzie and Jan {van Aardt}}

                }