Abstract
This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.
Highlights
LIght Detection And Ranging (LiDAR) technology determines the distance between ground objects and sensors by measuring the time a pulse of transmitted energy takes to return to the LiDAR sensor.When coupled with a ground referencing system, LiDAR sensors make dense, geo-referenced point elevation measurements [1,2,3]
LiDAR ground filtering methods may have variable performance when applied to different areas and terrain conditions
Accuracy assessment plays an important role in both ground filtering applications and algorithm development [77]; quantitative accuracy assessment has been a challenge for LiDAR ground filtering due to the lack of ground truth data
Summary
LIght Detection And Ranging (LiDAR) technology determines the distance between ground objects and sensors by measuring the time a pulse of transmitted energy takes to return to the LiDAR sensor. The dense LiDAR point clouds enable generation of highly accurate, high resolution DEMs. Second, surface features can be extracted based on a height context analysis of the. It is easier to identify slight elevation changes using dense LiDAR point clouds, making it easier to map regions with little textural variations, including variations in the surface of vegetation canopies [15,23]. LiDAR can be used to map ground elevations even in regions of dense vegetation because of multiple returns [15]. We discuss the conditions that lead to the performance levels of each algorithm This comparison and discussion is especially important for new users of LiDAR data. Seeking to select an appropriate ground filtering algorithm and for developers seeking to improve algorithms
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