Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES

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 / m 2 and an Optech Titan system with approximately 35 points / m 2 . 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). We offer an improved benchmark of classification methods by evaluating the classification accuracy exclusively on tree segments with minimal segmentation errors, which is shown to improve the classification accuracy by 2–3 percentage points. The ranking of the classification methods remains largely unchanged and the point transformer model still achieves the highest accuracy, thus confirming the results of the prior benchmarking study. Furthermore, we study the relation between classification accuracy and tree height to demonstrate the advantage of the point transformer model for small trees and minority species. The public release of this comprehensive dataset and the related methodology facilitate the development of deep learning models for accurate tree species classification using multispectral laser scanning, while also advancing research in ecological monitoring and sustainable forest management.