# Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

arXiv: https://arxiv.org/abs/2312.15084 · HTML: https://mlsanity.com/paper/2312.15084/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 90 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (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)
- [3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2003.13867/) (2020)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [Mix3D: Out-of-Context Data Augmentation for 3D Scenes](https://mlsanity.com/paper/2110.02210/) (2021)
- [Associatively Segmenting Instances and Semantics in Point Clouds](https://mlsanity.com/paper/1902.09852/) (2019)
- [3D Instance Segmentation via Multi-Task Metric Learning](https://mlsanity.com/paper/1906.08650/) (2019)
- [CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation](https://mlsanity.com/paper/2207.09778/) (2022)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation](https://mlsanity.com/paper/2203.14662/) (2022)
- [Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation](https://mlsanity.com/paper/2001.01349/) (2020)
- [Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds](https://mlsanity.com/paper/2010.04642/) (2020)
- [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)
- [A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2108.09522/) (2021)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [Towards accurate instance segmentation in large-scale LiDAR point clouds](https://mlsanity.com/paper/2307.02877/) (2023)
- [Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds](https://mlsanity.com/paper/1702.06188/) (2017)

## Built on by (most cited first, 23 of 23)

- [SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data](https://mlsanity.com/paper/2401.15739/) (2024)
- [ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds](https://mlsanity.com/paper/2506.16991/) (2025)
- [Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset](https://mlsanity.com/paper/2408.06507/) (2024)
- [Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds](https://mlsanity.com/paper/2502.06227/) (2025)
- [Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms](https://mlsanity.com/paper/2504.14337/) (2025)
- [Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery](https://mlsanity.com/paper/2506.04970/) (2025)
- [ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation](https://mlsanity.com/paper/2606.01549/) (2026)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS](https://mlsanity.com/paper/2503.14273/) (2025)
- [3D forest semantic segmentation using multispectral LiDAR and 3D deep learning](https://mlsanity.com/paper/2507.08025/) (2025)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)
- [Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap](https://mlsanity.com/paper/2509.06329/) (2025)
- [WHU-STree: A Multi-modal Benchmark Dataset for Street Tree Inventory](https://mlsanity.com/paper/2509.13172/) (2025)
- [treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds](https://mlsanity.com/paper/2509.03633/) (2025)
- [Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification](https://mlsanity.com/paper/2510.09367/) (2025)
- [NormalView: tree species classification from backpack and aerial lidar data using geometric projections](https://mlsanity.com/paper/2512.05610/) (2025)
- [LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark](https://mlsanity.com/paper/2603.03616/) (2026)
- [In-Field 3D Wheat Head Instance Segmentation From TLS Point Clouds Using Deep Learning Without Manual Labels](https://mlsanity.com/paper/2603.14309/) (2026)
- [SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes](https://mlsanity.com/paper/2604.21356/) (2026)
- [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)
- [CedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests](https://mlsanity.com/paper/2608.30149/) (2026)
- [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026)

## Abstract (arXiv)

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.