DOI: 10.5281/zenodo.18510441
title: Mangrove3D: Terrestrial Laser Scanning Dataset for Coastal Mangrove Forests

description: 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_18510441,  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.18510441},  url          = {https://doi.org/10.5281/zenodo.18510441},}

@misc{zhang2025perspectivelidarfeatureenricheduncertaintyaware,      title={Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation},       author={Fei Zhang and Rob Chancia and Josie Clapp and Amirhossein Hassanzadeh and Dimah Dera and Richard MacKenzie and Jan van Aardt},      year={2025},      eprint={2510.06582},      archivePrefix={arXiv},      primaryClass={cs.CV},      url={https://arxiv.org/abs/2510.06582}, }