Enhancing Ground Point Extraction in Airborne LiDAR Point Cloud Data Using the CSF Filter Algorithm
Keywords:
Remote Sensing, CSF Filter, Point Cloud, SAM, LIDARAbstract
Airborne laser scanning (ALS) is a remote sensing method widely recognized for its efficiency in acquiring data quickly and delivering accurate results. To ensure the reliability of ALS data, effective decontamination is crucial. This study aims to enhance the data quality of three distinct LIDAR datasets representing urban, rural, and forest environments by applying the CSF Filter algorithm in the CloudCompare software, an open-source tool widely used in point cloud processing. The impact of various data characteristics and input parameters on the filtering results was assessed through a series of comprehensive tests. The results of our analysis revealed a notable relationship between the selected parameters and the quality of the filtered data. Specifically, when the cover value within the CSF Filter parameters was increased, a corresponding increase in data loss was observed, leading to significantly flawed outcomes. These findings emphasize the importance of carefully selecting and fine-tuning the input parameters to avoid undesirable consequences. The findings underscore the importance of combining automated filtering algorithms with manual cleaning to achieve high-quality and reliable point cloud data for various geospatial analyses and applications.
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