FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at http://arxiv.org/abs/2309.01279

从激光扫描数据中精准分割单株树木的难题，制约了高效森林管理所需关键树木参数的评估工作，进而对诸多下游应用造成负面影响。尽管密集激光扫描可提供精细的三维表征，但从点云（point cloud）中自动分割树木及其结构仍颇具挑战。当前缺乏适配的基准数据集（benchmark dataset）且过度依赖小型数据集，这一现状限制了相关方法的研发进展。深度学习模型的兴起进一步加剧了对标准化基准数据集的需求。为填补上述空白，FOR-instance数据集作为一款全新的基准测试数据集，旨在借助密集机载激光扫描数据优化森林测量工作，助力研究人员推进森林三维场景的分割方法研发。在本仓库中，用户可获取由无人机（unmanned aerial vehicle，搭载Riegl传感器）采集的森林激光扫描点云数据，这些数据已依据单株树木（共计1130株）及语义类别完成手动分割标注。该点云数据被划分为五个数据集集合，分别对应挪威、捷克、奥地利、新西兰与澳大利亚的不同林区。本数据集可用于两类场景：一是基于开发集（dev data）研发新方法，二是基于测试集（test data）对已有方法进行性能测试。数据集的划分信息已存储于data_split_metadata.csv文件中。FOR-instance数据集的完整说明可查阅链接：http://arxiv.org/abs/2309.01279

搜集汇总

背景与挑战

背景概述

FOR-instance是一个无人机激光扫描数据集，包含1130棵树木的点云数据，覆盖五个国家的森林，用于语义和实例分割研究。数据集支持开发新方法或测试现有方法，提供了详细的数据分割和语义类别标注。