@inproceedings{21a7104a92a24aa49a4b4b2879c0633d,
title = "A New Drone Laser Scanning Benchmark Dataset for Characterization of Single-Tree and Forest Biophysical Properties",
abstract = "Survey-grade laser scanners suitable for drones (UAV-LS) allow the efficient collection of finely detailed three-dimensional (3D) information on tree structures allowing to resolve the complexity of the forest into discrete individual trees and species as well as into different component of the tree. Current developments are hindered by the limited availability of survey-grade UAV-LS data and by the lack of a publicly available benchmark dataset for developing and validating methods. We present a new benchmarking dataset composed of manually labelled UAV-LS data covering forests in different continents and eco-regions. Such data consists in single-tree point clouds, with each point classified as either stem, branches, and leaves. This benchmark dataset offers new possibilities to develop single-tree segmentation algorithms and validate existing ones.",
keywords = "Drone, forest, in-situ data, lidar, machine learning",
author = "Stefano Puliti and Pearse, \{Grant D.\} and Watt, \{Michael S.\} and Edward Mitchard and Ian McNicol and Magnus Bremer and Martin Rutzinger and Peter Surovy and Luke Wallace and Markus Hollaus and Rasmus Astrup",
year = "2021",
month = oct,
day = "21",
doi = "10.1109/IGARSS47720.2021.9553895",
language = "English",
isbn = "978-1-6654-4762-1",
series = "IEEE International Geoscience and Remote Sensing Symposium IGARSS",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "728--730",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
note = "2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 11-07-2021 Through 16-07-2021",
}