# TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds arXiv: https://arxiv.org/abs/2309.08471 ยท HTML: https://mlsanity.com/paper/2309.08471/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 73 references that are in the map. - First posted on arXiv 15 Sep 2023. ## Builds on (most cited first, 16 of 16) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017) - [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019) - [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020) - [SoftGroup for 3D Instance Segmentation on Point Clouds](https://mlsanity.com/paper/2203.01509/) (2022) - [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017) - [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021) - [Superpoint Transformer for 3D Scene Instance Segmentation](https://mlsanity.com/paper/2211.15766/) (2022) - [ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution](https://mlsanity.com/paper/2303.00246/) (2023) - [3D Instances as 1D Kernels](https://mlsanity.com/paper/2207.07372/) (2022) - [FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees](https://mlsanity.com/paper/2309.01279/) (2023) - [Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest](https://mlsanity.com/paper/2305.02651/) (2023) - [Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR](https://mlsanity.com/paper/1810.12536/) (2018) ## Built on by (most cited first, 14 of 14) - [Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms](https://mlsanity.com/paper/2504.14337/) (2025) - [SelvaBox: A high-resolution dataset for tropical tree crown detection](https://mlsanity.com/paper/2507.00170/) (2025) - [ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation](https://mlsanity.com/paper/2606.01549/) (2026) - [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025) - [FlowMark: Mask-Guided Video Watermarking](https://mlsanity.com/paper/2607.05261/) (2026) - [Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap](https://mlsanity.com/paper/2509.06329/) (2025) - [Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data](https://mlsanity.com/paper/2603.04683/) (2026) - [Estimating the Diameter at Breast Height of Trees in a Forest from RGB](https://mlsanity.com/paper/2505.03093/) (2025) - [NormalView: tree species classification from backpack and aerial lidar data using geometric projections](https://mlsanity.com/paper/2512.05610/) (2025) - [SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds](https://mlsanity.com/paper/2606.27491/) (2026) - [A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning](https://mlsanity.com/paper/2607.22129/) (2026) - [Growing a Stand, Not a Tree: Joint Canopy Generation Reproduces Crown Shyness](https://mlsanity.com/paper/2608.28692/) (2026) - [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026) - [ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes](https://mlsanity.com/paper/2609.36891/) (2026) ## Abstract (arXiv) Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are usually based on hand-crafted algorithms, such as identifying trunks and growing trees from them, and face difficulties in dense forests with overlapping tree crowns. In this study, we propose TreeLearn, a deep learning-based approach for tree instance segmentation of forest point clouds. TreeLearn is trained on already segmented point clouds in a data-driven manner, making it less reliant on predefined features and algorithms. Furthermore, TreeLearn is implemented as a fully automatic pipeline and does not rely on extensive hyperparameter tuning, which makes it easy to use. Additionally, we introduce a new manually segmented benchmark forest dataset containing 156 full trees. The data is generated by mobile laser scanning and contributes to create a larger and more diverse data basis for model development and fine-grained instance segmentation evaluation. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation on the benchmark dataset shows that TreeLearn performs as well as the algorithm used to generate its training data. Furthermore, the performance can be vastly improved by fine-tuning the model using manually annotated datasets. We evaluate TreeLearn on our benchmark dataset and the Wytham Woods dataset, outperforming the recent SegmentAnyTree, ForAINet and TLS2Trees methods. The TreeLearn code and all datasets that were created in the course of this work are made publicly available.