Mapping forest structure: A 3D deep learning benchmark for ground-based LiDAR

Abstract

carbon monitoring to fire risk analysis or habitat and biodiversity assessments. Ground-based LiDAR technologies, such as terrestrial (TLS) and mobile (MLS) laser scanning, enable precise 3D mapping of trees, understorey, and ground, offering rich structural detail. Yet, extracting meaning from these dense, unstructured point clouds remains a persistent challenge that requires reliable and scalable segmentation methods. Earlier segmentation approaches have struggled to generalize across diverse forest types and acquisition conditions. Recent advances in deep learning (DL), particularly architectures designed for 3D data, offer new opportunities for robust and scalable forest structure modelling using ground-based point clouds. Nevertheless, their implementation in forestry remains limited, partly due to the difficulty of obtaining large, accurately labelled point clouds for training and evaluation. In this study, we benchmark the performance of six different implementations of four state-of-the-art DL architectures (PointNeXt, SuperPoint Transformer, Point Transformer V3 and OA-CNN) on the public SegmentedForests dataset, which contains over 850 million labelled points from 14 plots representing a range of coniferous and broadleaf forests scanned with both TLS and MLS. We assess segmentation performance across four ecologically meaningful classes: ground, understorey, stems, and canopy. The best-performing model, Point Transformer V3, achieved an overall mean Intersection over Union (mIoU) of 81.9%, confirming the strong potential of transformer-based architectures for forest point cloud segmentation. The benchmark analysis considers how segmentation accuracy varies across forest type, sensor, understorey density and forest maturity. We observe clear differences in segmentation difficulty across conditions, with coniferous plots being consistently easier to segment than broadleaf plots, and TLS point clouds yielding higher accuracies than MLS acquisitions. Furthermore, a dense understorey was found to significantly improve classwise saliency despite increased terrain occlusion. Cross-testing confirmed high model stability when generalized to entirely unseen scenes. Together, these results provide the first comprehensive benchmark of modern DL models for forest segmentation from ground-based LiDAR point clouds and highlight key factors shaping their performance across forest structural diversity.

Citation

Laino, Diego; Cabo, Carlos; Janvier, Romain; Ordonez, Celestino; Bolanos, Rodolfo; Doerr, Stefan H.; Hudak, Andrew T.; Santin, Cristina. 2026. Mapping forest structure: A 3D deep learning benchmark for ground-based LiDAR. ISPRS Journal of Photogrammetry and Remote Sensing. 241: 253-272.