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Airborne Lidar Data Artifacts: What we know thus far
IEEE Geoscience and Remote Sensing Magazine ( IF 14.6 ) Pub Date : 2023-07-04 , DOI: 10.1109/mgrs.2023.3285261
Wai Yeung Yan 1
Affiliation  

Data artifacts are a common occurrence in airborne lidar point clouds and their derivatives [e.g., intensity images and digital elevation models (DEMs)]. Defects, such as voids, holes, gaps, speckles, noise, and stripes, not only degrade lidar visual quality but also compromise subsequent data-driven analyses. Despite significant progress in understanding these defects, end users of lidar data confronted with artifacts are stymied by the scarcities of both resources for the dissemination of topical advances and analytic software tools. The situation is exacerbated by the wide-ranging array of potential internal and external factors, with examples including weather/atmospheric/Earth surface conditions, system settings, and laser receiver–transmitter axial alignment, that underlie most data artifact issues. In this article, we provide a unified overview of artifacts commonly found in airborne lidar point clouds and their derivatives and survey the existing literature for solutions to resolve these issues. The presentation is from an end-user perspective to facilitate rapid diagnoses of issues and efficient referrals to more specialized resources during data collection and processing stages. We hope that the article can also serve to promote coalescence of the scientific community, software developers, and system manufacturers for the ongoing development of a comprehensive airborne lidar point cloud processing bundle. Achieving this goal would further empower end users and move the field forward.

中文翻译:

机载激光雷达数据工件:迄今为止我们所知道的

数据伪影在机载激光雷达点云及其衍生物[例如强度图像和数字高程模型(DEM)]中很常见。空隙、孔洞、间隙、斑点、噪声和条纹等缺陷不仅会降低激光雷达的视觉质量,还会影响后续的数据驱动分析。尽管在理解这些缺陷方面取得了重大进展,但面临伪影的激光雷达数据的最终用户却因传播主题进展的资源和分析软件工具的稀缺而受到阻碍。各种潜在的内部和外部因素加剧了这种情况,例如天气/大气/地球表面条件、系统设置和激光接收器-发射器轴向对准,这些因素是大多数数据伪影问题的根源。在本文中,我们对机载激光雷达点云及其衍生物中常见的伪像进行了统一概述,并调查了现有文献以寻找解决这些问题的解决方案。该演示从最终用户的角度出发,以促进在数据收集和处理阶段快速诊断问题并有效转介更专业的资源。我们希望本文还能促进科学界、软件开发商和系统制造商的联合,以持续开发全面的机载激光雷达点云处理包。实现这一目标将进一步增强最终用户的能力并推动该领域向前发展。
更新日期:2023-07-04
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