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).
Highlights
Since the airborne laser scanner (ALS) systems have been commercialized in the late 1990s, light detection and ranging (LiDAR) data collected from ALS systems have been widely adopted as a major source in geospatial information engineering such as city planning, mobile navigation, forest mapping, disaster response, and damage assessment
The following procedures are commonly required in most of the methods for reconstructing 3D building models with LiDAR data: (i) Separation of the point clouds that belong to buildings from other objects, so called filtering (ii) Segmentation of the surface patches that form shape of the roofs based on the geometric similarity
We evaluate the proposed method on five airborne LiDAR datasets including simulated and real datasets
Summary
Since the airborne laser scanner (ALS) systems have been commercialized in the late 1990s, light detection and ranging (LiDAR) data collected from ALS systems have been widely adopted as a major source in geospatial information engineering such as city planning, mobile navigation, forest mapping, disaster response, and damage assessment. Because of precise and accurate data acquisition capability, LiDAR is one of the most preferred remote sensing technologies. LiDAR sensors are not affected by geometric distortions unlike optical sensors (e.g., lens distortion, perspective distortion, and relief displacement). Shadow and variation of the brightness and contrast due to illumination condition affect quality of the optical image while LiDAR data is less influenced by such external factors. Existing approaches are classified as data-driven (or bottomup) and model-driven (or top-down) approaches. The datadriven approach might consider faithful details pertinent to the resolution of the input data, while the model-driven approach could rapidly create building models with appealing appearance [2]. In the model-driven case, building models are recognized by fitting the LiDAR measurements with
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