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SegmentedForests: a labelled dataset of terrestrial LiDAR point clouds for semantic segmentation of forests

Diego Laino, Carlos Cabo, Celestino Ordóñez, Rodolfo Bolanos, Romain Janvier, Federico Giulioni, Miriam Herrmann, Andrew Hudak, Russell Parsons, Cristina Santin

Forestry: An International Journal of Forest Research, Volume: 99, Issue: 2

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DOI (Published version): 10.1093/forestry/cpaf062

Abstract

Semantic segmentation of point clouds using deep learning (DL) has been the subject of research in forestry in recent years due to its potential applications. Several scientific and management disciplines, such as biodiversity monitoring, ecosystem carbon assessments, or forest management could bene...

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Published in: Forestry: An International Journal of Forest Research
ISSN: 0015-752X 1464-3626
Published: Oxford University Press (OUP) 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa71694
Abstract: Semantic segmentation of point clouds using deep learning (DL) has been the subject of research in forestry in recent years due to its potential applications. Several scientific and management disciplines, such as biodiversity monitoring, ecosystem carbon assessments, or forest management could benefit from this technique. However, it requires manual segmentation of point clouds to be used as training data. This process is highly labour-intensive and time-consuming, and there is a notable lack of publicly available datasets to support the development of accurate DL semantic segmentation models for forestry and forest ecology applications. Here, we present SegmentedForests, a curated dataset of manually segmented ground-based point clouds from forest plots, specifically designed to facilitate the training and validation of semantic segmentation models. This publicly available dataset contains >920 million labelled points from 14 forest plots, acquired using both terrestrial laser scanning (TLS) and mobile laser scanning (MLS) technologies. It covers two hectares of broadleaf, conifer, and mixed stands from different bioclimatic regions and features >1600 trees across 16 tree species. Each point cloud is labelled into multiple vegetation classes (up to 16), such as tree stems, branches, grass, shrubs, and down wood, as well as non-vegetation elements commonly present in forest scenes, including rocks, people, and stakes. Data splits to facilitate DL model development using our dataset are provided as well. The dataset is available at https://zenodo.org/records/17396681. By releasing this annotated dataset, we seek to address the critical need for publicly available, high-quality training data for DL models that perform semantic segmentation of ground-based point clouds in forest ecosystems.
Item Description: A correction has been published: Forestry: An International Journal of Forest Research, Volume 99, Issue 2, April 2026, cpag018, https://doi.org/10.1093/forestry/cpag018
Keywords: forestry; deep learning; artificial intelligence; TLS; supervised segmentation; 3D computer vision
College: Faculty of Science and Engineering
Funders: This work was supported by the UK NERC project(NE/T001194/1): ‘Advancing 3D Fuel Mapping for Wildfire Behaviour and Risk Mitigation Modelling’, the Spanish Knowledge Generation project (PID2021-126790NB-I00): ‘Advancing carbon emission estimations from wildfires applying artificial intelligence to 3D terrestrial point clouds’, (grant PRE2022-104159) funded by MCIN/AEI/10.13039/501100011033 and FSE+, research grant ‘FIREPROs’ (IDE/2024/000780) funded by the Principality of Asturias Government (Spain), COST Action 3DForEcoTech CA20118 supported by COST (European Cooperation in Science and Technology) and the US DoD SERDP/ESTCP projects (RC201025), (RC23-7626), and (RC20-1046).
Issue: 2

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