Comprehensive Study on Enhanced Accuracy Analysis of LIDAR Data : The Example of Skopje
Keywords:
Remote sensing, CSF Filter, DTM, LIDAR, Accuracy analysisAbstract
By harnessing LIDAR technology, a prominent remote sensing method widely employed today, we explore its efficacy as a rapid and dependable tool for data collection. We focus on generating a numerical terrain model by leveraging the CSF Filter algorithm within the accessible CloudCompare software to filter an urban LIDAR point cloud. This study involves meticulous manual intervention to eliminate noise points, followed by examining the creation of a numerical terrain model by varying cover values (0.1, 0.5, 1, 2, and 5) in the CSF Filter algorithm. Our investigation delves into calculating the volume disparity between a reference model meticulously crafted within a computer environment, integrating manual interventions, and models derived through the CSF Filter algorithm. This approach aims to identify the cover value that best approximates reality in filtering operations. The decryption of volume disparities between the computer-generated reference model and the CSF Filter algorithm sheds light on the most accurate filtering outcome. The results indicates that opting for a cover value of 5 yields the most significant divergence from the reference model, presenting a less accurate model. Conversely, selecting a cover value of 0.5 as input data offers the closest approximation to the truth. However, it remains evident that manual interventions are indispensable for refining filtering operations even in the most precise model derived from these investigations.
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