Data infrastructure for multitemporal airborne LiDAR point cloud analysis - Examples from physical geography in high mountain environments
Data infrastructure for multitemporal airborne LiDAR point cloud analysis - Examples from physical geography in high mountain environments
复制标题
用于多时相机载激光雷达点云分析的数据基础设施 - 高山环境中的自然地理示例
DOI：
10.1016/j.compenvurbsys.2013.11.004
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
J. Stötter
中科院分区：
文献类型：
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作者：
L. Rieg;V. Wichmann;M. Rutzinger;R. Sailer;T. Geist;J. Stötter
多时相地形激光雷达数据的应用已成为人造和自然环境中许多测绘和监测应用的标准。随着全区域、高分辨率、多时相数据集的可用性不断增加，以及人们对使用原始测量的 3D 地形 LiDAR 点云的兴趣日益浓厚，有关最佳数据存储和管理的挑战变得越来越重要。在过去十年中，我们获得了一个独特的 LiDAR 数据集，该数据集由覆盖奥地利西部和意大利北部不同地区的 30 多个飞行活动组成，旨在分析高山环境中的表面变化。每次飞行活动的数据集都在激光雷达专用信息系统（LIS，Laserdata Information System）中存储和管理，该系统具有客户端-服务器架构和空间关系数据库作为核心。该系统集成到开源地理信息系统中，可以无缝集成到操作空间数据处理和分析工作流程中。它允许多用户访问 3D 点云数据和附加属性，例如强度、回波数、全球定位系统时间、飞行路径轨迹信息和全波形属性（如果有）。在本文中，我们描述了可用的数据集、3D 点云存储和管理的方法以及数据库的结构。评估直接访问存储的 3D 点云数据的价值以及存储点云属性的重要性。进一步的重点在于数据管理和基础设施对于分析大面积和长时间序列的地形激光雷达数据的重要性。
The application of multi-temporal topographic LiDAR data has become a standard for many mapping and monitoring applications in man-made and natural environments. With increasing availability of area-wide, high-resolution, multi-temporal datasets and the increasing interest in working with the original measured 3D topographic LiDAR point clouds, challenges regarding optimal data storage and management are gaining in importance. During the last decade, an unique LiDAR dataset, consisting of over 30 flight campaigns covering different areas in western Austria and northern Italy, has been acquired with the purpose to analyse surface changes in high mountain environments. The datasets from each flight campaign are stored and managed in a LiDAR specific information system (LIS, Laserdata Information System), which has a client–server architecture and a spatial relational database as a core. The system is integrated into an open source Geographical Information System, which allows a seamless integration into operational spatial data processing and analysis workflows. It enables multi-user access to 3D point cloud data and additional attributes, such as intensity, return number, global positioning system time, flight path trajectory information and fullwaveform attributes if available. In this paper we describe the available dataset, the approach to 3D point cloud storage and management and the structure of the database. The value of direct access to the stored 3D point cloud data is assessed as well as the importance of storing point cloud attributes. A further focus lies on the importance of the data management and infrastructure for the analysis of large areas and long time series of topographic LiDAR data.