BorFIT: a novel LiDAR-based training dataset for individual tree segmentation and species detection in northern boreal forests

Abstract

BorFIT is a novel northern boreal forest training dataset designed to assist in the segmentation of individual trees and the detection of species from uncrewed aerial vehicle (UAV)-LiDAR point clouds, thus contributing to deep learning-based forestry applications. Recent advancements in AI-supported individual tree detection have shown significant progress; however, satisfactory results remain elusive in dense and structurally-complex boreal forests. We compiled a training dataset designed to remedy this issue. BorFIT comprises 384 UAV-LiDAR point clouds, each with an area of 20 m × 20 m, in the form of reference plots, with up to 200 manually segmented and species classified trees per point cloud. The BorFIT training dataset is derived from UAV-LiDAR surveys at 146 sites carried out between 2021 and 2024 in East Siberia (Yakutia), northwest Canada, and Alaska (USA). The sites were selected along bioclimatic gradients to represent the circumboreal region. From each UAV-LiDAR transect derived point cloud, we extracted a minimum of four reference plots (each 20 m × 20 m) based on maximum tree heights within the plots to systematically sample the apparent tree density gradient. We manually segmented identifiable trees within each reference plot point cloud leading to 16 530 individual trees in total. Following individual tree segmentation, we trained four random forest classifiers for different regions to predict the species of every segmented tree. The predicted tree species include: Picea mariana Britton, Sterns & Poggenb., Picea sitchensis Bong. Carrière, Picea glauca Moench, Voss, Pinus contorta Douglas ex Loudon, Abies lasiocarpa Hook., Nutt., Larix laricina Du Roi, K. Koch, Betula papyrifera Marshall, Betula neoalaskana Regel, Ashburner McAll., Populus balsamifera  L., Populus tremuloides Michx., Pinus sylvestris Thunb. and Alnus spp. The data offer the means for 3D space analysis of species distribution and stand structure around the circumboreal region. Furthermore, it can be used as a training dataset for artificial intelligence (AI) applications, and thereby improve our understanding of the boreal forest's vegetation reorganization in response to significant global warming (https://doi.org/10.1594/PANGAEA.980505, Kruse et al., 2026).