About the book

LiDAR is a non-contact measurement used to acquire three-dimensional (3D) topographic information on visible surfaces of objects. Nowadays, LiDAR usage is not only limited to surveying as it was originally developed but also rapidly offers a lot of applications from civil engineering, urban modeling, mapping, automobile, and agriculture. Although a recent development of LiDAR technology allows capturing a large area in a short time with high accuracy, identifying scanning parameters or trajectories is still based on the rule of the thumb. Datapoint clouds from a single scan are highly accurate but combining the point clouds from multiple scans is still an issue. Although a lot of methods based on geometric analysis and machine learning have been developed for the recognition, classification, and extraction of objects from the point clouds, robust and efficient methods are still adopted for various types of laser scanning point clouds and data quality is still questioned. Moreover, converting raw point clouds to 3D geometric models still requires intensive labor work. As such, generating 3D digital models and extracting semantic information from the point clouds is one of the important keys to generating digital twins, but the automatic process is still a high challenge. The cost, time, and quality of the 3D model can be a barrier to this process. This book aims to investigate the state of the art in using LiDAR technologies in a role to create digital twins.