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

2026-04-27

AI Summary

This study addresses the scarcity of publicly available multispectral airborne laser scanning (MS-ALS) datasets with high-quality field validation, which has hindered individual tree species classification research. We present the first open MS-ALS dataset, comprising 6,326 individually delineated trees across nine species in southern Finland, acquired using the HeliALS and Optech Titan dual-system sensors to capture three-wavelength point clouds. Ground truth was collected via an efficient and scalable field protocol. Leveraging deep learning models—including point cloud segmentation and Point Transformer architectures—we achieve high-accuracy species classification, demonstrating particularly strong performance for small-sized and rare tree species. Our results validate the efficacy of multispectral ALS data for fine-grained species discrimination and establish a benchmark platform to advance future research in this domain.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Data transparency and provenance

Abstract

The shift from stand-level to individual-tree-level forest assessments supports improved biodiversity mapping, particularly in boreal ecosystems where tree species like aspen (Populus tremula L.) play a keystone role. While airborne laser scanning (ALS) is the standard for such inventories, a major limitation is the small number of publicly available ALS datasets containing high-quality, field-validated reference data. Furthermore, open multispectral ALS datasets with high-quality field reference data are completely lacking despite the potential of multispectral ALS data for tree species classification. This paper presents and details an open multispectral ALS dataset used in a recent international benchmarking study of machine learning and deep learning methods for tree species classification 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$^2$. We provide a detailed description of field data collection techniques developed in the study to facilitate the collection of high-quality ground truth data in an efficient and scalable manner. Additionally, our article presents new analyses on species classification using multispectral data building upon the initial findings of Taher et al. (2026). Furthermore, we study the relation between classification accuracy and tree height to highlight the versatility of the open dataset and to demonstrate the advantage of the point transformer model for small trees and minority species.

Problem

• multispectral airborne laser scanning
• tree species classification
• open dataset
• field reference data
• individual-tree-level assessment

Innovation

• multispectral airborne laser scanning
• tree species classification
• open dataset
• point cloud
• Point Transformer