in: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
COMPARISON OF METHODS FOR AUTOMATED BUILDING EXTRACTION FROM 
HIGH RESOLUTION IMAGE DATA 
G. Vozikis 
GEOMET Ltd., Faneromenis 4, 15561 Holargos-Athens, GREECE 
george.vozikis@geomet.gr 
KEY WORDS: Photogrammetry, Building , Detection , Transformation, Model, Pattern 
ABSTRACT: 
This paper discusses a comparison analysis of different methods for automated building extraction from aerial and spacebome 
imagery. Particularly approaches employing the Hough Transformation, Pattern Recognition Procedures and Texture Analysis are 
examined. Throughout this investigation advantages and disadvantages of the mentioned methods are examined, in order to see 
which procedures are suitable for extracting the geometric building properties, and thus to automatically create a DCM (Digital City 
Model). The examined data sets consist of panchromatic imagery coming from both very high resolution satellites, as well as line 
scanning aerial sensors. A quantitative and qualitative assessment will help to evaluate the previously mentioned procedures. 
1. INTRODUCTION 
Automated building extraction from high resolution image data 
(either airborne or spacebome) is becoming more and more 
mature. Everyday new techniques are investigated and the 
results are getting more and more reliable, while the degree of 
automation increases. Each building extraction method is of 
course coupled to certain pros and cons. The use of the Hough 
Transformation has proven to be a very promising tool in the 
frame of the automated creation of Digital City Models 
(DCMs), by extracting building properties from optical data. 
But also approaches based on Image Matching or Texture 
Analysis seem to provide usable results. A DCM is described 
through the outlines of buildings outlines of an urban area. 
Vertical walls are assumed, and the elevation information of 
these buildings can be taken from a DSM (Digital Surface 
Model). The creation of the DSM and the assignement of the 
elevation value is not discussed in this paper, thus when 
mentioning DCMs we actually mean the Model that holds the 
2D outline-information of a building. 
The goal of this paper is to conclude for which kind of data sets 
and accuracy pretensions a certain approach is recommendable. 
Moreover, the reachable degree of automation is also examined, 
in order to see how reliable results are that were produced 
without human interaction. 
Table 1 : Examined data sets. 
Sensor 
Location 
GSD (m) 
Extents (km) 
ADS40 
Valladolid, Spain 
0.25 
1 x 1 
HRSC-AX 
Bern, Switzerland 
ca. 0.3 
0.2 x 0.3 
Quickbird 
Denver, USA 
0.6 
16.9 x 16.5 
IKONOS 
Athens, Greece 
1 
9.7 x 12.3 
Orb view 3 
Orange, USA 
1 
0.6 x 0.7 
Altogether, five different datasets, coming from airborne and 
spacebome sensors, were examined. These datasets depict 
urban regions with varying building sizes, patterns and 
densities. It should be mentioned here that only subsets have 
been used for the investigations. 
2. DESCRIPTION OF WORKFLOWS 
2.1 Hough Transformation 
The proposed workflow for automated building extraction from 
image data by employing the Hough Transformation has been 
thoroughly described in Vozikis (2004). Figure 1 shows the 
major steps of the process. 
Figure 1: Proposed workflow for automated building extraction 
from image data 
All steps in this workflow are highly automated and human 
interaction is reduced to a minimum. 
In the following the 4 major steps are briefly described.