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

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

Abstract 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 learnin...

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Main Authors: Stefano Puliti, Emily R. Lines, Jana Müllerová, Julian Frey, Zoe Schindler, Adrian Straker, Matthew J. Allen, Lukas Winiwarter, Nataliia Rehush, Hristina Hristova, Brent Murray, Kim Calders, Nicholas Coops, Bernhard Höfle, Liam Irwin, Samuli Junttila, Martin Krůček, Grzegorz Krok, Kamil Král, Shaun R. Levick, Linda Luck, Azim Missarov, Martin Mokroš, Harry J. F. Owen, Krzysztof Stereńczak, Timo P. Pitkänen, Nicola Puletti, Ninni Saarinen, Chris Hopkinson, Louise Terryn, Chiara Torresan, Enrico Tomelleri, Hannah Weiser, Rasmus Astrup
Format: Article
Language:English
Published: Wiley 2025-04-01
Series:Methods in Ecology and Evolution
Subjects:
biodiversity
deep learning
lidar
point cloud classification
remote sensing
single‐tree inventory
Online Access:https://doi.org/10.1111/2041-210X.14503
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https://doi.org/10.1111/2041-210X.14503

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