公开了日本柏树与杉木林的无人机激光雷达树木标注数据集。
CedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests
- 人工标注34个样方的无人机激光雷达点云，匹配1627棵树木。
- 提供22个样方的地面激光雷达数据，用于树干/非树干语义标注。
- 适合做温带人工林树木分割、属性预测及多平台激光雷达研究。
基于安装在无人机（UAV）上的激光雷达（LiDAR）获取的单株树木测量数据，对森林清查、生态系统监测和可持续森林管理具有重要价值。近年来，机器学习的发展推动了点云分析方法的需求，尤其在单株树木分割方面，但公开可用的温带森林无人机激光雷达标注数据集仍十分有限。本文介绍了CedarCypress3D，一个在日本杉木（Cryptomeria japonica）和日本扁柏（Chamaecyparis obtusa）人工林中采集的经人工标注的无人机激光雷达数据集。该数据集包含来自两个地形特征不同的地点共34个圆形样方的无人机激光雷达点云与实地调查数据，并有22个样方的地面激光雷达点云作为补充。共计1,627棵树在实地普查中被测量并人工标注，以对应无人机激光雷达点云中的树木。对于拥有地面激光雷达数据的子集，还为无人机激光雷达数据中的树点赋予了语义标签（树干与非树干）。CedarCypress3D为温带人工林中单株树木实例分割与语义分割方法的开发与评估提供了高质量标注数据，还可支持树木属性预测与多平台激光雷达分析研究。数据集已公开发布于 https://doi.org/10.5281/zenodo.22168721。
原文摘要 · Abstract (English)
Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information for forest inventory, ecosystem monitoring, and sustainable forest management. Recent advancements in machine learning have increased the demand for annotated datasets to develop and evaluate point cloud-based approaches, especially for individual tree segmentation. However, publicly available annotated UAV-LiDAR datasets in temperate forests are limited. In this article, we present CedarCypress3D, a manually annotated UAV-LiDAR dataset collected in Japanese cedar (Cryptomeria japonica) and Japanese cypress (Chamaecyparis obtusa) plantations in Japan. The dataset consists of UAV-LiDAR point clouds and field survey measurements from 34 circular plots across two sites with different topographic characteristics, along with terrestrial LiDAR point clouds available for a subset of 22 plots. A total of 1,627 trees were measured in the census field survey and manually annotated to match the corresponding trees in the UAV-LiDAR point clouds. For the subset of plots with terrestrial LiDAR data, semantic labels (i.e., stem and non-stem) were additionally assigned to tree points in the UAV-LiDAR data. CedarCypress3D provides high-quality annotated UAV-LiDAR data for developing and evaluating individual tree instance segmentation and semantic segmentation methods in temperate planted forests. The dataset can also support research on tree attribute prediction and multi-platform LiDAR analysis. The dataset is publicly available at https://doi.org/10.5281/zenodo.22168721.
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