Generation of 3-D Large-Scale Maps Using Lidar Point Cloud Data

Abstract

Abstract. 3-Dimensional geospatial data is essential for creating and utilizing real-world visualizations for analyzing infrastructure design improvements. However, techniques exist such as Total Station, Global Positioning System (GPS), and Google Earth data. Traditionally 3D maps were constructed using simple measurements of angles and distances, providing a planimetric representation of the area with a limited number of cross-sections to reflect the built-up area. However, Terrestrial laser scanning (TLS) is an efficient technique for capturing dense point clouds to construct detailed information models for large-scale built-up areas. In recent years, advancements in the measurement of speed, reduction of size and cost, and portability of these technologies have revolutionized 3-D data collection. This article discusses the methodological framework and 3-D mapping accuracy by adopting terrestrial laser scanning to map a built-up area having different features. Point cloud data collected in the form of scans were processed and registered using Faro Scene software with an overall registration error of 14 mm (RMSE). Finally, it creates a 3-D map of the built-up area having planimetric and elevation accuracy analyses. It will be used for spatial analyses or simulating the interaction of surface and subsurface processes contributing to urban and rural planning development in a 3-D GIS environment and for smart cities. The method presented here provides a different way for achieving large-scale maps with greater accuracy, which will be applicable in identifying and monitoring the deformations in the built-up areas and in various disaster mitigation.

Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics

“…Presently, the acquisition and processing of 3D data have become key technologies [ 1 , 2 ]. These technologies play a crucial role in various fields, including innovative basic surveying and mapping [ 3 , 4 , 5 ], the creation of high-precision maps for autonomous driving [ 6 , 7 , 8 , 9 ], geological landslide monitoring [ 10 , 11 ], and the preservation of cultural heritage [ 12 , 13 , 14 , 15 , 16 ]. Point cloud data, as a primary form of 3D data, are highly favored for their ability to precisely depict the physical space’s shape and structure.…”