A comprehensive study on enhanced accuracy analysis of LiDAR data

Abstract

LIDAR technology, a prominent remote sensing technology widely employed today, offers a highly reliable means of swiftly and accurately gathering data. This research project aims to generate a Digital Terrain Model (DTM) from a LIDAR dataset featuring urban attributes. The chosen tool for this endeavor is the CSF Filter algorithm within Cloud Compare, an open-source software, with an emphasis on assessing the model's precision. Within the CSF Filter algorithm, we examined the accuracy of the Surface Approximation Mesh (SAM) when various cover values were employed: 0.1, 0.5, 1, 2, and 5. Our investigation primarily revolved around calculating the volume disparity between a manually created reference model within a computer environment and the models generated through filtering. This analysis allowed us to pinpoint the most suitable parameter value for creating an accurate model. The results indicated that opting for a cover value of 0.5 produced the most accurate model. Notably, when a cover value of 5 was chosen for the input parameter, the largest disparities were observed between the resulting model and the reference model.