. Istanbul 2004 
    
AUTOMATIC BUILDING EXTRACTION AND 3-D CITY MODELING FROM LIDAR 
DATA BASED ON HOUGH TRANSFORMATION 
KAZUO ODA TADASHI TAKANO TAKESHI DOIHARA RYOSUKE SHIBASAKI** 
ASIA AIR SURVEY CO, LTD. 
*THE UNIVERSITY OF TOKYO 
Commission III, PS WG III/3 
KEY WORDS: LIDAR, Modeling, City, Graphics, Edge, Visibility Analysis 
ABSTRACT: 
This paper proposes a new method of LIDAR data processing to automatically extract building and 3-D city modeling. This method 
exploits image processing technique including Hough transformation. The process of building extraction starts with DSM 
segmentation according to elevation values. Among these fragments, extracting process limits candidates of building fragments to 
those that have elevation higher than a specified threshold value and the DEM at the same location. Hough transformation is 
executed to boundary pixels of each candidate of building fragment and then fit to linear segments. Each fragment is polygonized 
with these line segments. After the extraction, 3-D-building model is created with these polygons such that each polygon has vertical 
wall from the top of building to the ground (DEM). Texture images are pasted onto each building, including walls. The most 
appropriate texture image is selected from aerial images according to geometry between building faces and external parameters of 
the photos. The method has been tested with LIDAR data of Shinjuku and Ginza area in Tokyo. 
1. INTRODUCTION 
Advances of information technology are changing the concept 
of map form simple 2-D space expression into 3-D real world 
model constructed in cyberspace. There are two main problems 
for 3-D model applications. The first one is how to express and 
define 3-D model for general use or specific applications. The 
other one concerns methods to build up 3-D model at a 
reasonable cost performance. This paper focuses on the method 
to build up 3-D city model with LIDAR data. 
The most typical commercialized method of 3-D city modelling 
with LIDAR data simply stands up buildings and houses in 2-D 
map with height values of footprints that fall into building 
polygons. This method, however, cannot express details of 
building shape, since building polygons in 2-D map simply 
trace outside boundaries of buildings. Another type of 3-D city 
model expression uses original LIDAR DSM. This type of 3-D 
model can express the details of city, but the amount of data 
tends to be too large and hard to manage or view in PC without 
special devices. 
This paper presents an automated method for 3-D city model 
production with LIDAR data and aerial photo images, which 
can be applied to production of 3-D map for infrastructure. First, 
LIDAR DSM image is divided into fragments where DSM 
pixels have almost the same elevation value. Hough 
transformation extract straight lines and curves of building 
boundaries and constructs them into 3-D shapes. Extracted 
polygons are shaped into prisms with planar walls and aerial 
photo images are pasted onto each faces of building model as 
texture images according to geometrical information. Thus 
building models are more realistic with detail of shapes and 
texture images. 
This paper first overviews the strategy of building extraction 
and construction and introduces building extraction with Hough 
transformation. Some experimental results with tests are 
presented in the following section with LIDAR data of Shinjuku 
and Ginza area in Tokyo, Japan. 
2. THE STRATEGY OF THE METHOD 
The strategy of the proposed algorithm consists of two parts. 
The first part is building extraction where building polygons are 
extracted from DSM and aerial photos. The second part is 3-D 
modelling where 3-D-building model is created with these 
polygons such that each polygon have vertical wall from the top 
of building to the ground. 
2.1 Building Extraction 
The schema of the algorithm is shown in Figure 1.This method 
treats DSM data as “image”, and exploit image processing 
technique including Hough transformation. As pre-processing, 
LIDAR data are converted in raster format and DEM is 
extracted by minimum filtering. Details of the image processing 
sequence are as follows. 
2.1.1 DSM Segmentation: The process of building 
extraction starts with segmentation, where raster DSM is 
divided into fragments where DSM pixels have almost the same 
elevation value. Among these fragments, extracting process 
limits candidates of building fragments to those that have higher 
elevation more than a specified threshold value and the DEM at 
the same location. Jaggies around DSM masks are cleaned up 
with morphological dilation and erosion. A DSM fragment 
extracted here is called “DSM masks”. To avoid extraction of 
trees, cars and other objects, city block polygons in existing 
digital map data can be applied to omit DSM masks outside the 
polygons. 
2.1.2 Edge Extraction: 
Edge Extraction of the DSM image is executed with some edge 
operator, such as Canny operator or other zero-crossing 
operators. If DSM resolution is too poor to extract edge lines, 
aerial photo images can be used alternatively. In resultant edge 
image, each edge should be thinned into 1-pixel width edge 
lines.