# SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

arXiv: https://arxiv.org/abs/2401.15739 · HTML: https://mlsanity.com/paper/2401.15739/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning](https://mlsanity.com/paper/2312.15084/) (2023)
- [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)
- [Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning](https://mlsanity.com/paper/2101.09154/) (2021)
- [AI applications in forest monitoring need remote sensing benchmark datasets](https://mlsanity.com/paper/2212.09937/) (2022)
- [Towards accurate instance segmentation in large-scale LiDAR point clouds](https://mlsanity.com/paper/2307.02877/) (2023)

## Built on by (most cited first, 17 of 17)

- [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)
- [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)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests](https://mlsanity.com/paper/2606.08206/) (2026)
- [Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches](https://mlsanity.com/paper/2503.05630/) (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)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)
- [Weakly-Supervised Learning for Tree Instances Segmentation in Airborne Lidar Point Clouds](https://mlsanity.com/paper/2508.15646/) (2025)
- [TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching](https://mlsanity.com/paper/2602.01501/) (2026)
- [Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data](https://mlsanity.com/paper/2603.04683/) (2026)
- [treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds](https://mlsanity.com/paper/2509.03633/) (2025)
- [NormalView: tree species classification from backpack and aerial lidar data using geometric projections](https://mlsanity.com/paper/2512.05610/) (2025)
- [Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory](https://mlsanity.com/paper/2605.02201/) (2026)
- [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026)
- [ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation](https://mlsanity.com/paper/2610.03403/) (2026)

## Abstract (arXiv)

This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The study evaluates the model's performance based on platform (ULS, MLS) and data density, testing five scenarios with varying input data, including sparse versions, to gauge adaptability and canopy layer efficacy. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation, validated on diverse point cloud datasets. Results show point cloud sparsification enhances performance, aiding sparse data handling and improving detection in dense forests. The model performs well with >50 points per sq. m densities but less so at 10 points per sq. m due to higher omission rates. It outperforms existing methods (e.g., Point2Tree, TLS2trees) in detection, omission, commission rates, and F1 score, setting new benchmarks on LAUTx, Wytham Woods, and TreeLearn datasets. In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests. This contributes to future ecological modeling and forest management advancements.