Principles of processing and three-dimensional modelling through lidar data for applied research of the urban environment

Abstract

Introduction. The 3D modeling technology of the urban environment using LiDAR survey data expands the possibilities of urban research. With proper use of various methods, models and algorithms for processing and analyzing LiDAR data, they can significantly facilitate and open up new opportunities for many applications discussed in this paper. The main research objective of the paper is to review methods for analyzing LiDAR survey data in urban studies and to present individual elements of the author’s optimization of these methods. Results. LiDAR data obtained as a result of laser scanning of the earth's surface from a certain vehicle form a three-dimensional terrain model in the point cloud form of varying density degrees. The post-processing of such data can branch out into many applications, which are discussed in this paper. The building extraction from a cloud of LiDAR points is performed using complex computational operations, the essence of which is to calculate the points of separate planes of the buildings roofs and then extract these points for 3D building modeling. There are many approaches to building extraction that aim to either improve the quality and accuracy of the extracted models or to speed up the data processing. Finding the optimal solution for 3D modeling of the urban environment is an urgent task in this area of research. Tracking changes in urban buildings involves comparing digital models of urban areas for different time periods in order to obtain the changes volume for each building. In a similar fashion, LiDAR data is used to assess damage to buildings by creating random points on the buildings walls and comparing their displacements before and after the damage. The population estimate using LiDAR data is based on a comparison of population data for census tracts with data on the number, area and volume of buildings in the same tracts obtained from processed LiDAR data. As a result, the expected population in each individual building can be calculated. Roads extraction from LiDAR data is performed by creating an image of the LiDAR laser pulse intensity and then comparing this image with a digital surface model. The article provides an example of a scheme for such road extraction. In addition, methods for extracting and mapping power lines by filtering the corresponding points are also considered. The ability to determine the exact size, slope, and exposure of a building's roof plane also makes it possible to estimate the potential level of solar radiation received by the roof, which can contribute to the optimal placement of solar power plants. Such an assessment may cause some difficulties, which are discussed in the article. The article proposes various optimization solutions for the considered methods, which were partially implemented in the ELiT software. In addition to effective tools for automatic data processing, the ELiT Project also provides an environment for high-quality visualization of results in a standard web-GIS interface. Conclusions. LiDAR data, in combination with efficient algorithms for processing and filtering data, greatly facilitates the solution of a number of tasks related to area monitoring and urban planning. In the future, the high accuracy of LiDAR data and the possibility of their visualization in GIS will make it possible to analyze the urban development features in order to identify the urban geosystemic properties of the city.