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Tree segmentation and change detection of large urban areas based on airborne LiDAR
Computers & Geosciences ( IF 4.4 ) Pub Date : 2021-08-05 , DOI: 10.1016/j.cageo.2021.104900
Anett Fekete 1 , Mate Cserep 1
Affiliation  

As the utilization of LiDAR (Light Detection and Ranging) is getting more affordable and available for a wider audience, the analysis of point clouds constructed by laser scanning is earning more attention. Airborne LiDAR is especially useful in the analysis and classification of land objects. We are able to determine if they are natural or artificial objects and what changes occurred to them throughout time by examining multi-temporal data. The goal of our research was to define a completely automatized methodology for the segmentation of vegetation (specifically trees) in urban environment, followed by the qualification and quantification of change detection. Our proposed algorithm provides a robust approach designed to scale dynamically to large areas, in contrast to existing methods that require manual or semi-supervised human interaction and can only be applied on relatively small areas. The algorithm was tested on parts of the Dutch and the Estonian altimetry archives, point cloud datasets that provide several terabytes of data. It was proved to be an effective method for the qualified and quantified change detection of trees, including height and volume changes.



中文翻译:

基于机载激光雷达的大城市区域树木分割与变化检测

随着 LiDAR(光检测和测距)的使用变得越来越实惠,并且可供更广泛的受众使用,通过激光扫描构建的点云分析越来越受到关注。机载 LiDAR 在陆地物体的分析和分类中特别有用。我们能够通过检查多时态数据来确定它们是自然物体还是人造物体,以及它们在整个时间里发生了什么变化。我们研究的目标是定义一种完全自动化的方法来分割城市环境中的植被(特别是树木),然后对变化检测进行限定和量化。我们提出的算法提供了一种鲁棒的方法,旨在动态扩展到大区域,与需要手动或半监督人机交互且只能应用于相对较小区域的现有方法形成对比。该算法在荷兰和爱沙尼亚测高档案的部分内容上进行了测试,点云数据集提供了数 TB 的数据。它被证明是一种有效的方法,可以对树木的高度和体积变化进行合格和量化的变化检测。

更新日期:2021-08-11
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