Accuracy analysis of urban LİDAR point cloud with CSF algorithm

Thanks to the use of LIDAR technology, which is one of the remote sensing technologies that are used quite a lot today, it comes across as a very fast and reliable data collection tool. In our thesis, the aim of creating a numerical terrain model by filtering an urban LIDAR point cloud using the Cloth Simulation Filter (CSF) algorithm contained in the open source CloudCompare software is to create a digital terrain model. After cleaning the noise points with manual interventions, the creation of a numerical terrain model is studied by using different cover values (0.1, 0.5, 1, 2 and 5) as input data in the CSF algorithm. By calculating the volume disparity between the reference model created in a computer environment with manual interventions and the models obtained using the CSF algorithm, it allows determining which cover value gives a closer to the truth result in Deceleration operations. When the result data are examined, it is observed that the biggest difference between the model and the reference model is formed as a result of choosing the cover value of 0.5 and Deciphering the most accurate model, while choosing the cover value of 5 as input data. Even in the most accurate model obtained as a result of the examinations, it is seen that manual filtering operations require interventions.