COMPARATIVE ANALYSIS OF DTM EXTRACTION FROM AIRBORNE LIDAR POINT CLOUD DATA WITH ADAPTIVE TIN FILTERING, CLOTH SIMULATION FILTER, AND PROGRESSIVE MORPHOLOGICAL FILTER METHODS

• Main Author: Perkasa Sulung, Panji
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/73532
• Institution: Institut Teknologi Bandung

LiDAR is a system that can generate digital surface elevation data quickly and at a relatively affordable cost. In the last few decades, the application of LiDAR data in mapping has been increasingly used because of its high resolution and accuracy. However, raw LiDAR data cannot be processed into a Digital Terrain Model (DTM) because it still contains point cloud data that is reflected from various objects on the Earth's surface. To create a DTM, ground and non-ground objects must be separated from LiDAR point cloud data through a filtering process. Many filtering methods have been developed to extract LiDAR point cloud data that are on the ground automatically. This final project was made with the aim of understanding the characteristics and differences of each LiDAR point cloud filtering method in dealing with various topographical conditions. The results obtained are in the form of LiDAR point cloud data that has gone through the filtering process and also the DTM surface. The methodology used is literature review and data processing using three LiDAR point cloud data filtering algorithms, namely Adaptive Triangulated Irregular Network (ATIN), Cloth Simulation Filter (CSF), and Progressive Morphological Filter (PMF). The results obtained through research in this final project are in the form of comparison data of LiDAR point clouds that have gone through the filtering process and the resulting DTM representation. It is hoped that the results of the research in this Final Project can be a solution for choosing the right filtering method for LiDAR point cloud data in accordance with the characteristics of the LiDAR point cloud data used.