XXIX-B3, 2012 
^, 2002. Efficient 
n Proceedings of the 
70. 
ject extraction from 
onal Geoscience and 
). 
method for building 
tion LIDAR data. In: 
etry, Remote Sensing 
95-301. 
/osselman, G., 2009. 
mobile and airborne 
tional Archives of 
Spatial Information 
n and reconstruction 
IDAR point clouds. 
mote sensing, 48(3), 
ficient RANSAC for 
r Graphics Forum, 
r P., 2007. Hough- 
thms for automatic 
JIDAR data. In: The 
Remote Sensing and 
12. 
iction using planar 
:: The International 
ensing and Spatial 
) Building Model 
ound Plans. In: The 
Remote Sensing and 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
RECONSTRUCTION OF BUILDING OUTLINES IN DENSE URBAN AREAS BASED ON 
LIDAR DATA AND ADDRESS POINTS 
M. Jarzabek-Rychard 
Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Science, Poland 
malgorzata.jarzabek-rychard @up.wroc.pl 
Commission III, WG IIV2 
KEY WORDS: LIDAR, Point Cloud, Algorithms, Building, Reconstruction 
ABSTRACT: 
The paper presents a comprehensive method for automated extraction and delineation of building outlines in densely built-up areas. 
A novel approach to outline reconstruction is the use of geocoded building address points. They give information about building 
location thus highly reduce task complexity. Reconstruction process is executed on 3D point clouds acquired by airborne laser 
scanner. The method consists of three steps: building detection, delineation and contours refinement. The algorithm is tested against 
a data set that presents the old market town and its surroundings. The results are discussed and evaluated by comparison to reference 
cadastral data. 
1. INTRODUCTION 
1.1 Motivation 
The two dimensional building outlines reconstruction is 
expected to be a fully automated process that produces a high 
level of detail output. Development of numerous disciplines 
dealing with spatial data, like real estate industry or GIS, has 
caused increasing requirements for building footprints. 
Therefore, current high interest is to implement automatic 
outlining of existing buildings followed by change detection 
algorithms. (Champion et al., 2008). In addition, extraction of 
building boundaries can also be an important step towards 3D 
buildings modelling. 
Objects are commonly reconstructed from data captured by 
laser scanning. Increasing accessibility and operating ability of 
LIDAR sensors allow acquisition of very dense point clouds 
that leads to detailed modelling. Reconstruction process starts 
from object detection, which is complex task crucial for entire 
modelling. Many proposed methods available in literature rely 
on additional available data, like spectral images or topographic 
databases (Awrangjeb et al, 2010, Haala et al. 1998, 
Vosselman, Dijkman, 2001). Such information significantly 
improves determination of building boundaries and its accuracy. 
However, for some areas especially in developing countries, 
additional information sources are difficult to obtain. According 
to above, a method presented below utilizes building address 
points, that gives initial information about buildings location. 
The points are easily accessible from open web portals. 
1.2 Geocoded address data 
Each address point is assigned to one building and has a 
random location within planar building outline. The point 
position is determined by x and y coordinates. À set of points, 
used in that work for algorithm testing, was obtained from 
regional agency cadastral database. It is worth to mention, that 
there are several on-going projects that aim at an 
implementation of a free, participatory, community oriented 
119 
geocoding services. Among them are for example 
OpenGeocoding (http://www.opengeocoding.org), Open 
Addresses  (http://openaddresses.org) or OpenStreetMap 
(http://www.openstreetmap.org). The main objective of these 
projects is to provide a worldwide free address database with 
focus on areas where 2D databases are not completed (Behr and 
Rimayanti, 2008). Web based services on a worldwide level aim 
at collection of geocoded address data, like building postal 
addresses and coordinates, in order to make them freely 
available. Utilization of such information facilitates building 
reconstruction and completion of topographic databases. 
1.3 Aims 
The objective of this work is to develop a comprehensive 
method for automated extraction and delineation of complex 
building outlines in densely built-up areas. Objects are extracted 
from raw 3D point cloud acquired by airborne LIDAR sensors. 
The method is unique with respect to other algorithms used for 
building detection because it benefits from including building 
address points. They give initial information about building 
location and serve as the seed points during building detection. 
The presented reconstruction approach is focused on the areas 
where buildings are tightly adjacent to each other creating 
complex and irregular outlines. Especially in such scenes full 
automated and exact building extraction poses a challenge. 
Incorporated address points simplify detection process and 
highly reduce the complexity of the entire modelling task. 
The presented approach to building outline determination is 
solved in three steps. First, individual buildings are detected 
based on their address points—position. Second, the detected 
regions of interest are delineated. Third, the initial contours are 
subjected to the refinement. The examples presented in this 
paper were computed using 3D point clouds with the density of 
12 points/m?. The data was captured by airborne LiDAR full 
waveform system in the old town of Brzeg (Poland). The results 
show the high potential of the presented approach.