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

• URL: http://arxiv.org/abs/2604.24370v1
• Date: Mon, 27 Apr 2026 12:05:37 GMT
• Title: Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES
• Abstract summary: The shift from stand-level to individual-tree-level forest assessments supports improved biodiversity mapping.<n>While airborne laser scanning (ALS) is the standard for such inventories, a major limitation is the small number of publicly available ALS datasets.<n>This paper presents and details an open multispectral ALS dataset used in a recent international benchmarking study.
• Score: 4.296012381827889
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models [45.7884146182982]
  We introduce a synthetic diagnostic framework for aerial-view vehicle detection that combines text-guided generation, attribute-controlled editing, and automated attribute verification.<n>Our results show that controlled synthetic probing can predict real-domain performance gaps and guide efficient data collection.<n>The proposed diagnostic framework is modular and can incorporate alternative generative or vision-language models as capabilities evolve.
  arXiv  Detail & Related papers  (2026-07-02T19:11:57Z)
• NormalView: sensor-agnostic tree species classification from backpack and aerial lidar data using geometric projections [2.076162610275395]
  We present a sensor-agnostic projection-based deep learning method for classifying tree species from point cloud data.<n>NormalView embeds local geometric information into two-dimensional projections, in the form of normal vector estimates.<n>We inspected the effect of multispectral intensity information on classification performance.
  arXiv  Detail & Related papers  (2025-12-05T10:52:55Z)
• Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms [3.9167717582896793]
  Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing and tree segmentation.<n>This study addresses these gaps by conducting a benchmark of machine learning and deep learning methods for tree species classification.
  arXiv  Detail & Related papers  (2025-04-19T16:03:49Z)
• Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds [47.679877727066206]
  Assessment of forest biodiversity is crucial for ecosystem management and conservation.<n>Deep learning-based fusion of close-range sensing data from 2D orthophotos and 3D airborne laser scanning (ALS) point clouds can reliable assess the biodiversity potential of forests.<n>BioVista dataset comprises 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark.
  arXiv  Detail & Related papers  (2025-01-03T09:42:25Z)
• An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds [67.96583737413296]
  We propose an enhanced classification method based on adaptive multi-scale fusion for MPCs with long-tailed distributions.<n>In the training set generation stage, a grid-balanced sampling strategy is designed to reliably generate training samples from sparse labeled datasets.<n>In the feature learning stage, a multi-scale feature fusion module is proposed to fuse shallow features of land-covers at different scales.
  arXiv  Detail & Related papers  (2024-12-16T03:21:20Z)
• Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe [0.0]
  Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. In this study, we employed TomoSense, a 3D tomographic dataset, which utilizes a stack of single-look complex (SLC) images.
  arXiv  Detail & Related papers  (2024-11-19T22:25:26Z)
• Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset [1.2771525473423657]
  FOR-species20K benchmark was created, comprising over 20,000 tree point clouds from 33 species. This dataset enables the benchmarking of DL models for tree species classification. The top model, DetailView, was particularly robust, handling data imbalances well and generalizing effectively across tree sizes.
  arXiv  Detail & Related papers  (2024-08-12T21:47:15Z)
• A Closer Look at Deep Learning Methods on Tabular Datasets [78.61845513154502]
  We present an extensive study on TALENT, a collection of 300+ datasets spanning broad ranges of size.<n>Our evaluation shows that ensembling benefits both tree-based and neural approaches.
  arXiv  Detail & Related papers  (2024-07-01T04:24:07Z)
• PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests [0.0]
  We present the PureForest dataset: a large-scale, open, multimodal dataset designed for tree species classification. Most current public Lidar datasets for tree species classification have low diversity as they only span a small area of a few dozen annotated hectares at most. In contrast, PureForest has 18 tree species grouped into 13 semantic classes, and spans 339 km$2$ across 449 distinct monospecific forests.
  arXiv  Detail & Related papers  (2024-04-18T10:23:10Z)
• Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources [44.99833362998488]
  We develop an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses.
  arXiv  Detail & Related papers  (2024-01-22T18:42:31Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.