发表机构
Forestry and Forest Products Research Institute; Green Kogyo（森林综合研究所; Green Kogyo）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CedarCypress3D，为温带人工林提供含1627棵树标注的无人机激光雷达数据集，可支撑单株树木分割等相关研究，且部分样地附带地面激光雷达数据。
AI 中文摘要
搭载于无人机（UAV）的激光雷达（LiDAR）获取的单株树木测量数据，为森林清查、生态系统监测及可持续森林管理提供了重要信息。近年来机器学习的进展增加了对带标注数据集的需求，以开发和评估基于点云的方法，尤其是针对单株树木分割的方法。然而，温带森林中公开可用的带标注无人机激光雷达数据集有限。本文提出CedarCypress3D，这是一个在日本的日本柳杉（Cryptomeria japonica）和日本扁柏（Chamaecyparis obtusa）人工林中采集的带人工标注的无人机激光雷达数据集。该数据集包含两个具有不同地形特征的样地中34个圆形样地的无人机激光雷达点云和野外调查测量数据，其中22个样地的子集还提供了地面激光雷达点云。在普查野外调查中，共测量了1627棵树，并进行人工标注以匹配无人机激光雷达点云中对应的树木。对于具有地面激光雷达数据的样地子集，还为无人机激光雷达数据中的树点分配了语义标签（即树干和非树干）。CedarCypress3D为开发和评估温带人工林中的单株树木实例分割和语义分割方法提供了高质量的带标注无人机激光雷达数据，该数据集还可支持树木属性预测和多平台激光雷达分析的研究，其可通过此https URL公开获取。
英文摘要
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.
Comments15 pages, 8 figures
