# Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset arXiv: https://arxiv.org/abs/2511.00653 ยท HTML: https://mlsanity.com/paper/2511.00653/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 48 of its 196 references that are in the map. - First posted on arXiv 1 Nov 2025. ## Builds on (most cited first, 30 of 48) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019) - [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020) - [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019) - [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018) - [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019) - [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020) - [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025) - [Mask3D: Mask Transformer for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2210.03105/) (2022) - [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021) - [OneFormer3D: One Transformer for Unified Point Cloud Segmentation](https://mlsanity.com/paper/2311.14405/) (2023) - [Towards Data-Efficient Detection Transformers](https://mlsanity.com/paper/2203.09507/) (2022) - [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) - [SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data](https://mlsanity.com/paper/2401.15739/) (2024) - [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (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) - [TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds](https://mlsanity.com/paper/2309.08471/) (2023) - [OAM-TCD: A globally diverse dataset of high-resolution tree cover maps](https://mlsanity.com/paper/2407.11743/) (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) ## Built on by (most cited first, 2 of 2) - [Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES](https://mlsanity.com/paper/2604.24370/) (2026) - [A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning](https://mlsanity.com/paper/2607.22129/) (2026) ## Abstract (arXiv) Individual tree segmentation (ITS) from LiDAR point clouds is fundamental for applications such as forest inventory, carbon monitoring and biodiversity assessment. Traditionally, ITS has been achieved with unsupervised geometry-based algorithms, while more recent advances have shifted toward supervised deep learning (DL). In the past, progress in method development was hindered by the lack of large-scale benchmark datasets, and the availability of novel data formats, particularly multispectral (MS) LiDAR, remains limited to this day, despite evidence that MS reflectance can improve the accuracy of ITS. This study introduces FGI-EMIT, the first large-scale MS airborne laser scanning benchmark dataset for ITS. Captured at wavelengths 532, 905, and 1,550 nm, the dataset consists of 1,561 manually annotated trees, with a particular focus on small understory trees. Using FGI-EMIT, we comprehensively benchmarked four conventional unsupervised algorithms and four supervised DL approaches. Hyperparameters of unsupervised methods were optimized using a Bayesian approach, while DL models were trained from scratch. Among the unsupervised methods, Treeiso achieved the highest test set F1-score of 52.7%. The DL approaches performed significantly better overall, with the best model, ForestFormer3D, attaining an F1-score of 73.3%. The most significant difference was observed in understory trees, where ForestFormer3D exceeded Treeiso by 25.9 percentage points. An ablation study demonstrated that current DL-based approaches generally fail to leverage MS reflectance information when it is provided as additional input features, although single channel reflectance can improve accuracy marginally, especially for understory trees. A performance analysis across point densities further showed that DL methods consistently remain superior to unsupervised algorithms, even at densities as low as 10 points/m2.