Determination of Building Model Key Points Using Multidirectional Shaded Relief Images Generated from Airborne LiDAR Data

Abstract

Light detection and ranging (LiDAR) data collected from airborne laser scanner system is one of the major sources to reconstruct Earth’s surface features. This paper presents a method for detecting model key points (MKPs) of the buildings using LiDAR point clouds. The proposed approach utilizes shaded relief images (SRIs) derived from the LiDAR data. The SRIs based on the concept of the shape from shading could provide unique information about individual surface patches of the building roofs. The main advantage of the proposed approach is to detect directly MKPs, which are primitives for 3D building modeling, without segmenting point clouds. Depending on the location of the light source, the SRIs are created differently. Therefore, integration of the multidirectional SRIs created from different locations of the light source could provide more reliable results. In addition, the vertical exaggeration (i.e., scaling Z-coordinates) is also beneficial because constituent surface patches of the roofs in the SRIs created with vertically exaggerated LiDAR data are more distinguishable. To determine the MKPs of the roofs, building data was separated from other objects using modified marker-controlled watershed algorithm in accordance with criteria to specify buildings such as area, height, and standard deviation. This process could remove the unnecessary objects such as trees, vegetation, and cars. The curvature scale space (CSS) corner detector was used to determine MKP since this method is robust to geometric changes such as rotation, translation, and scale. The proposed method was applied to simulated and real LiDAR datasets with various roof types. The experimental results show that the proposed method is effective in determining MKPs of various roof types with high level of detail (LoD).

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

“…The least squares fitting plane L i is obtained from the nearest neighbors, and the normal vector of the plane L i is used as the normal vector n i of the target point. Using principal component analysis method [25], the problem of computing the normal vector of the plane L i is transformed into solving the eigenvectors corresponding to the minimum eigenvalues for the covariance matrix Q obtained from the nearest neighbors, as shown in Equation (1).…”

“…The rapid development of light detection and ranging (LiDAR) technology has demonstrated substantial advantages in achieving an accurate understanding of the threedimensional (3D) world [1][2][3]. The accurate reconstruction of the fine geometric models of buildings, based on terrestrial laser scanning point clouds, has become an urgently needed research hotspot in the fields of mapping, remote sensing, and computer vision [4][5][6][7].…”

Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model

“…Each value of R is converted to an 8-bit image with range from 0 to 255. The multidirectional SRIs are generated by changing the location of the light source [26]. Four SRIs were generated with light sources of NW, NE, SE, and SW direction (see Figure 5).…”