SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
Authors: David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Théo Defosse, Frédéric Moore, Philippe Nolet, +2 more
Organizations: Northern Robotics Laboratory, Universit´e Laval, Qu´ebec, QC, G1V 0A6, Canada · D´epartement des sciences du bois et de la forˆet, Universit´e Laval, Qu´ebec, QC, G1V 0A6, Canada · Institut des Sciences de la Forˆet temp´er´ee, Universit´e du Qu´ebec en Outaouais, Ripon, QC, J0V 1V0, Canada
Abstract
Interest in forestry automation is growing alongside rapid advances in deep learning. In particular, tree detection and taxonomic classification are seen as core tasks required for automating field surveys and forestry equipment. These operations must often be performed in under-canopy settings, which pose challenging conditions for perception systems, including heavy occlusion, variable lighting, and dense vegetation. Despite this necessity, current work has yet to properly establish the feasibility of simultaneously executing tree detection and taxonomic classification in natural forests, as available datasets primarily focus on urban settings or on a limited number of species. To address this gap, we present SilvaScenes, a benchmark dataset for instance segmentation of tree species from under-canopy images in natural forests. Collected across five bioclimatic domains in Quebec, Canada, our dataset features 1421 trees from 28 species, with segmentation masks for pixel-precise tree trunk detection and fine-grained species annotations from forestry experts. We demonstrate the relevance and difficult nature of SilvaScenes by evaluating modern deep learning approaches, showing that while trunk segmentation is feasible, with a top mean average precision (mAP) of 69.9% and mean average recall (mAR) of 76.4%, species-aware segmentation remains a significant challenge with an mAP and an mAR of only 39.2% and 68.6%, respectively. Alongside additional experiments, we highlight key challenges, namely that species imbalance and tree occlusion figure among the most pressing issues for precise segmentation and identification. Meanwhile, higher image resolutions contribute to significant performance gains and will likely prove fundamental to these tasks moving forward. Our dataset, source code, and models will be made available at https://github.com/norlab-ulaval/SilvaScenes.
Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservation, and provide reference data for biomass and carbon mapping. Point clouds from airborne and ground-based laser scanning are currently the most suitable data source to rapidly derive such information at scale. Recent advancements in deep learning improved segmenting and classifying individual trees and identifying semantic tree components. However, deep learning models typically require large amounts of annotated training data which limits further improvement. Producing dense, high-quality annotations for 3D point clouds, especially in complex forests, is labor-intensive and challenging to scale. We explore strategies to reduce dependence on large annotated datasets using self-supervised and transfer learning. Our objective is to improve performance across three tasks: instance segmentation, semantic segmentation, and tree classification using realistic and operational training sets. We observe improvements across all tasks, compared to training from scratch, evaluated with their respective metrics. For instance segmentation, self-supervised learning combined with domain adaptation improves AP50 by 16.98%. For semantic segmentation, self-supervised learning alone improves mIoU by 1.79%. For tree classification, hierarchical transfer learning improves mean Jaccard by 6.07%. To simplify use and encourage uptake, we integrated the tasks into a unified framework, streamlining the process from raw point clouds to tree delineation, structural analysis, and species classification. Pretrained models reduce energy consumption and carbon emissions by ~21%. This open-source contribution aims to accelerate operational extraction of individual tree information from laser scanning point clouds to support forestry, biodiversity, and carbon mapping.
Aldino Rizaldy, Fabian Ewald Fassnacht, Ahmed Jamal Afifi +3
We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds. The model combines a serialization-based Point Transformer v3 backbone with a lightweight semantic head and a tree-focused cross-attention mask decoder. Semantic predictions restrict instance decoding to tree-class voxels, while instance-aware query initialization, one-to-many seed supervision, and asymmetric mask scoring improve separation in dense and structurally complex stands. We further introduce FOR-instance v3, an expanded benchmark comprising 427 scenes and 26,496 annotated trees across diverse biomes, forest structures, and LiDAR platforms. On the FOR-instanceV2 test split, SegmentAnyTreeV2 achieves 90.5% precision, 80.2% recall, 85.0% F1, 90.7% coverage, and 87.6% semantic mIoU, outperforming previous learning-based methods in both instance detection and mask completeness. Zero-shot evaluation on independent sites further demonstrates strong cross-domain generalization.
The shift from stand-level to individual-tree-level forest assessments supports improved species mapping and biodiversity monitoring, particularly in boreal ecosystems where tree species like aspen (Populus tremula L.) play a keystone role. Airborne laser scanning (ALS) is the standard for such inventories, but a major limitation for developing improved species classification methods is the small number of publicly available ALS datasets containing high-quality, field-validated reference data. Recently, multispectral ALS data has shown promise for tree species classification, but the progress is hindered by the lack of open multispectral ALS datasets with high-quality field reference data. This paper presents and details an open multispectral ALS dataset for tree species classification that was used before its public release for an international benchmarking study of machine learning and deep learning classification methods in a related publication by Taher et al.,(2026). The dataset comprises 6326 segment-level point clouds of individual trees representing nine species in southern Finland. The point cloud data has been acquired using two multispectral laser scanning systems each operating at three laser wavelengths: a helicopter-borne system (HeliALS) with a point density exceeding 1000 points\m2 and an Optech Titan system with approximately 35 points\m2. Furthermore, we present a crowdsourcing application that facilitates the collection of high-quality field reference data of tree species in an efficient and scalable manner. Our article showcases the versatility of the open dataset by presenting new analyses on species classification using multispectral data building upon the initial findings of Taher et al.,(2026).