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AUTOMATIC BUILDING DETECTION FROM LIDAR POINT CLOUD DATA 
Nima Ekhtari, M.R. Sahebi, M.J. Valadan Zoej, A. Mohammadzadeh 
Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, P O Box 15875-4416, Tehran, 
Iran - nima_el983@yahoo.com, sahebi@kntu.ac.ir, valadanzouj@kntu.ac.ir, ali_mohammadzadeh2002@yahoo.com 
Commission, WG IV/3 
KEY WORDS: Building Detection, LIDAR, DSM, DTM, Normalized DSM 
ABSTRACT: 
This paper proposes an automatic system which detects buildings in urban and rural areas by the use of first pulse return and last 
pulse return LIDAR data. Initially both first and last pulse return points are interpolated to raster images. This results to two Digital 
Surface Models (i.e. DSM) and a differential DSM (i.e. DDSM) is computed by them. Then using a height criterion, rough and 
smooth regions of the DDSM are found. Then last pulse points lying inside smooth regions are filtered using a simplified Sohn 
filtering method to find the so called on-terrain points by which the Digital Terrain Model (i.e. DTM) is generated. The normalized 
DSM (i.e. nDSM) is calculated using first pulse-derived DSM and the calculated DTM. Afterwards two separated classifications are 
applied on the nDSM. The final results of classifications are a set of nDSM pixels belonging to building roofs. The accuracy of the 
proposed algorithm is evaluated using some metrics and has proved an overall accuracy of 95.1% and a correctness equal to 98.3% 
and a completeness factor equal to 89.5% which show the level of the efficiency and accuracy of the system. 
1. INTRODUCTION 
Nowadays there is an increasing demand for 3D urban models 
produced from Earth Observation data. Such a model contains 
all buildings of a city superposed on an accurate DTM. 3D 
urban models are being widely used in the development of 3D 
GIS databases which has many applications in utility services, 
traffic management, air pollution control, etc. 
A 3D City Modeling procedure consists of three phases that is 
building detection, building extraction, and building 
reconstruction. The purpose of first phase is to detect (the pixels 
belonging to) some regions representing buildings. The subject 
of second phase is to compute the geometry of polygons which 
best fit the detected pixels. The third phase aims to compute and 
fit the best planar roof type for buildings. 
Among all available methods to extract building models of a 
city, those who use integrated data sources appear more 
successful since the weakness points of either data sources can 
be compensated by the other one. Many researches have been 
done on the combination of high-resolution imagery and 
LIDAR data to detect and extract buildings (Sohn and Dowman 
2007; Schenk and Csatho 2002; Rottensteiner et al. 2005; Guo 
and Yasuoka 2002). 
Considering the capability of dense LIDAR data, there is no 
necessity to involving any aerial images in the building 
detection task. Even single-source data systems can work faster 
and more automatically. Many researches have shown the 
capability of LIDAR data in detection and extraction of the 
buildings (Vosselman 1999; Maas and Vosselman 1999; Zhang 
et. al 2006). 
Two ways are often utilized to identify building measurements 
from LIDAR data. One is to separate the ground, buildings, 
trees, and other measurements from LIDAR data simultaneously. 
The more popular way is to separate the ground from non 
ground LIDAR measurements first and then identify the 
building points from non-ground measurements [Zhang et. al 
2006]. The proposed algorithm here is also among the latter 
way. 
Although there have been some automatic and semi-automatic 
methods of building modeling proposed by many researchers, 
all the three phases still can be studied and developed more. The 
fact that the accuracy of building detection phase has a 
dominant direct effect on the buildings extraction task, suggests 
that there is a need for more accurately detected building pixels. 
Because the geometry of buildings are extracted wherever 
building pixels are detected. This paper develops a building 
detection system. 
Our building detection algorithm (system) is therefore a single 
source data system, since it only utilizes LIDAR data to detect 
buildings. Our system generates some Digital Elevation Models 
by the interpolation of LIDAR points. The result of our system 
is presented in a raster format. In other words our algorithm 
uses vector data (3D coordinated points) as inputs and gives the 
information in raster format (Building pixels with their 
elevation). 
2. STUDY AREA 
The LIDAR point cloud data used to evaluate our system 
comprises of two recorded laser pulse returns; First pulse and 
Last pulse return points. FP (i.e. First Pulse return) points are 
those recorded from the first reflection of the laser pulse. As a 
result they might belong to the edges or surfaces of objects on 
the terrain rather than the ground beneath them. While the LP 
(i.e. Last Pulse return) points are more likely to belong to the 
terrain surface, especially for points of vegetation-covered 
regions and those near walls of buildings. That’s why we prefer 
LP points to create DTM and FP points to create DSM. 
The dataset used to evaluate the accuracy of our algorithm 
contains both the FP and LP data. The points of either have a