Accuracy assessment of Digital Terrain Model (DTM) Constructed Cloth Simulation Filter (CSF) and Multi Curvature Classification (MCC) algorithm on UAV LiDAR dataset / Mohamad Khairan Mohd Asri

Unmanned aerial vehicles (UAVs) offer a cost-effective and efficient solution for acquiring high-resolution data over small areas, enabling the generation of orthophotos and three-dimensional point clouds. These point clouds serve as the foundation for deriving accurate digital terrain models (DTMs). However, challenges arise in processing airborne laser scanning point clouds to generate DTMs, particularly when dealing with different land cover types and slopes. This study aims to evaluate the effectiveness of open-source software algorithms for ground classification in lidar point clouds and the subsequent generation of accurate DTMs. Two algorithms, the Cloth Simulation Filter (CSF) in CloudCompare and the Multiscale Curvature Classification (MCC) in Global Mapper, were tested for this purpose. The study encompasses two test areas, one featuring a flat terrain and the other a hilly terrain. Comparative analysis of software packages, including Global Mapper and CloudCompare, was conducted based on their processing methods and point cloud accuracy. The evaluation was carried out using qualitative and quantitative approaches, considering specific criteria tailored to each area's distinct land cover and slope characteristics. The findings presented in this study provide valuable recommendations for selecting suitable software for processing airborne laser scanning data in the Batu Kawan area.