BUILDING BOUNDARY EXTRACTION 
FROM HIGH RESOLUTION IMAGERY AND LIDAR DATA 
Liang Cheng*, Jianya Gong, Xiaoling Chen, Peng Han 
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, 
Wuhan University, Wuhan, China - lcheng.geo@gmail.com, 
-(jgong, cxl)@lmars.whu.edu.cn, -zb34xiaowu@163.com 
KEY WORDS: Building boundary, High resolution image, Lidar data, Data integration 
ABSTRACT: 
Building boundary data are necessary for the real estate industry, 3D city models and many other applications. In this study, a novel 
approach integrated high resolution imagery and Lidar data is proposed for automatically obtaining building boundaries with precise 
geometric position and details. The high resolution images were used to directly extract the building boundaries with precise 
geometric position, our approach is focused on improving the correctness and completeness of the extracted boundaries by 
integrating Lidar data. The approach consists of four steps: Lidar data processing, building image generation, line segment extraction, 
and boundary segment selection. Firstly, the segmented building points need to be determined from raw Lidar data. Then, a building 
image is generated by processing an original image using a bounding rectangle and a buffer, which are derived from the segmented 
building points. Based on the building image and rough principal direction constraints, an algorithm is proposed to estimate the 
principal orientations of a building, which ensures the accuracy and robustness of the subsequent line segments extraction. Finally, 
an algorithm based on Lidar point density analysis and Kmeans clustering is proposed to identify accurate boundary segments from 
the extracted line segments dynamically. The experiment results demonstrated that the proposed approach determined building 
boundaries well. 
1. INTRODUCTION 
Building boundary data are necessary for the real estate 
industry, city planning, homeland security, flood management, 
and many other applications. The extraction of building 
boundary is also a crucial and difficult step toward generating 
city models. The automatic extraction of a building boundary 
from an image has been a research issue for decades; many 
related studies have been reported. McGlone and Shufelt (1994) 
extracted building boundaries by calculating vanishing points 
using the Gaussian Sphere technique to detect horizontal and 
vertical lines. Mohan and Nevada (1989) described an approach 
based on perceptual grouping for detecting and describing 3D 
objects in complex images and applied the method to detect and 
describe complex buildings in aerial images. Xu, et.al., (2002) 
employed a Gabor filter to eliminate noisy edges, and then used 
a Normalized Central Contour Sequence Moment to select 
regular contours. A detailed review of techniques for the 
automatic extraction of buildings from aerial images was made 
by Mayer (1999). However, a difficult problem still exists in 
automated extraction of building boundaries because it is 
almost impossible to automatically distinguish building 
boundaries from other line segments in a high accuracy only 
based on aerial imagery. Moreover, a robust solution for 
boundary extraction is needed because too much complex 
information is contained in an image, especially for a very high 
resolution image. 
Many studies also focused on boundary extraction by using 
Lidar point clouds. Weidner (1996) used the difference between 
DSM and DTM to determine the building outlines. Vosselman 
& Sander (2001) and Haala & Brenner (1999) used plan maps 
to support boundary extraction from Lidar points. Many related 
papers have been published (Morgan and Habib, 2002; 
Rottensteiner and Briese, 2002; Overby, et.al., 2004; 
Vosselman and Kessels, 2005; Brenner, 2005). In general, it is 
hard to obtain a detailed and geometric precise boundary only 
using Lidar point clouds considering its low spatial resolution. 
To eliminate noise effects and get building boundaries with 
precise geometric position, some researchers used the minimum 
description length (MDL) method to regularize the ragged 
building boundaries (Weidner and Forstner, 1995; Suveg and 
Vosselman, 2004). Zhang et.al.(2006) used Douglas-Peucker 
algorithm to remove noise in a footprint, then adjusted the 
building footprints based on estimated dominant directions. 
Sampath and Shan (2007) performed building boundary tracing 
by modifying a convex hull formation algorithm, then 
implemented boundary regularization by a hierarchical least 
squares solution with perpendicularity constraints. However, 
regularization quality is also dependent on the point density of 
Lidar data; and limitation of Lidar data resolution and errors in 
filtering processes may cause obvious offset and artefacts in the 
final regularized building boundary (Sampath and Shan, 2007). 
Although it is difficult to obtain building boundaries with 
precise geometric position using Lidar data, Lidar data is able 
to directly provide measured three-dimensional points. On the 
other hand, although very high resolution images can provide 
building boundaries with precise geometric position, the 
accuracy of automatic boundary extraction is still in a low level. 
It seems to be valuable to extract building boundaries by 
integrating very high resolution imagery and Lidar data. 
However, how to integrate the two data sources for building 
boundary extraction is still a problem; few approaches with 
technical details has been published (Rottensteiner, 2005). 
* Corresponding author. 
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