Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR-species20K dataset

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F25%3AN0000032" target="_blank" >RIV/00027073:_____/25:N0000032 - isvavai.cz</a>

Alternative codes found

RIV/44555601:13520/25:43899776

Result on the web

<a href="https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14503?getft_integrator=clarivate&src=getftr&utm_source=clarivate" target="_blank" >https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14503?getft_integrator=clarivate&src=getftr&utm_source=clarivate</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/2041-210X.14503" target="_blank" >10.1111/2041-210X.14503</a>

Result language

angličtina

Original language name

Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR-species20K dataset

Original language description

Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5). 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10618 - Ecology

Project

<a href="/en/project/LUC23023" target="_blank" >LUC23023: A step towards advanced forest inventory through laser scanning and 3D modelling. Can field surveys be minimized?</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

METHODS IN ECOLOGY AND EVOLUTION

ISSN

2041-210X

e-ISSN

2041-2096

Volume of the periodical

16

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

801-818

UT code for WoS article

001506701400013

EID of the result in the Scopus database

2-s2.0-105002053486