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LINEAR FEATURE EXTRACTION OF BUILDINGS FROM TERRESTRIAL LIDAR 
DATA WITH MORPHOLOGICAL TECHNIQUES 
Jianghua Zheng a,b,c *, Tim McCarthy b , A. Stewart Fotheringham b , Lei YaiT 
a College of Resources and Environment Science, Xinjiang University, Oasis Ecology Key Lab of National Education Bureau, 
Urumqi 830046, Xinjiang, China, itslbs@126.com 
'’National Center for Geocomputation Ireland, Maynooth Ireland 
C RS & GIS institute of Peking University, Beijing, 100871, China 
Key words: LIDAR, Terrestrial, Feature, Extraction, Edge, Detection, Building 
Abstract: 
LiDAR has been a major interest of photogrammetry to acquire three dimensional objects. It has shown its promising capabilities in 
building virtual reality applications, such as virtual campus and virtual historic sites. However, point clouds of LiDAR data always 
occupy a large sum of storage capacity. This blocks further fast processing of LiDAR data to combine with GIS to build virtual 
reality. The research focused on linear feature extraction of buildings from terrestrial LiDAR data. To obtain linear features of 
buildings is one of the critical steps to realize minimization of redundant data and high efficiency of data processing. The paper 
discussed the procedure of linear features extracting of buildings and mainly put forward edge detection algorithms based on fractal 
dimension theory. Triangular method was chosen to obtain fractal dimension values of grids. The algorithm was not only effective 
and efficient to detect building edges, but also helpful for segmenting the building and nature objects. Future work was also discussed 
in the end. 
1. INTRODUCTION AND BACKGROUND 
Since LiDAR is typically not weather dependent and can be 
carried out over areas where a conventional survey would be 
extremely difficult or damaging, it has increasing applications 
in various fields. Up to the end of 2006, there are approximately 
150 LiDAR systems provided by commercial manufacturers 
worldwide active today (BC-CARMS, 2006). Some of them are 
TopScan, Optech, TopSys and Leica. China also has its own 
LiDAR systems. ShuKai Li led a group and developed an 
airborne LiDAR system in 1996. Qingquan Li developed a 
terrestrial LiDAR system in his lab (Lai Xudong, 2005). 
At present, most software packages of LiDAR data processing 
have inability to efficiently handle the immense volumes of data 
captured by LiDAR sensors and don’t provide modules to 
realize the function linear feature extraction. Many researchers 
pay a lot of interests on this topic to improve the situation. Most 
of them tried to realize the function with airborne LiDAR data 
processing. Digital Surface Models (DSM) was put forward to 
push the function realization (Paolo G., Bijan H. 2000). 
Texture-based segmentation was used to tell different regions of 
landform (Arko L. and Alfred S. 2004). Fuzzy reasoning and 
information fusion techniques were also put forward for 
demonstration (F. Samadzadegan. 2004). And some researchers 
tried to use multi-resolution wavelet filters to get better result of 
linear feature detection from LiDAR data (Samuel P. K. and R. 
H. Cofer. 2005). To improve the character of real-time 
processing, parallel algorithm for linear feature detection was 
demonstrated to be useful (Manohar M. and Paul C. 2006). 
Some researches just face the challenges of automatic image 
segmenting. Automatic construction of building footprints from 
airborne LIDAR Data has been testified (Zhang K. Q., Yan J. H. 
and Chen S. C., 2006). Recently, expectation-maximization 
method was used to classify aerial LiDAR data (Suresh K. L., 
Darren M. F. and David P. H, 2007). Some researchers 
combined several methods to improve effect of the segmenting 
and classifying, such as region growing, size filter and k-means 
classification methods were used to extract building class from 
LiDAR DEMs (George M. and Nikolaos K. 2007). Most of 
these researches were focused on airborne LiDAR data 
processing. Terrestrial LiDAR system is relative simple. 
However, its data has different characters from that of airborne 
LiDAR system. The raw airborne LiDAR data are recorded 
alone the flight line when the data were collected. However 
terrestrial LiDAR data are collected from different static spots. 
And it has relatively small detecting radius. Usually, it can 
obtain more abundant and precise information of objects. These 
make some difference in data processing of raw terrestrial 
LiDAR. 
This research focused on linear feature extraction of buildings 
from terrestrial LiDAR data. Point clouds of LiDAR data 
always occupy a large sum of storage capacity. It is common 
that data volume of one building may take more than 200MB 
and there are some files in the CLICK (the Center for Lidar 
Information, Coordination and Knowledge) website that are 
about 2 GB large (Qi Chen, 2007). To obtain linear features of 
buildings is one of the critical steps to realize minimization of 
redundant data and high efficiency of data processing. This 
research is initial work carried out at the National Centre for 
Geocomputation Ireland for virtual campus building in 2007. 
The paper discussed the procedure of linear features extracting 
of buildings and mainly put forward edge detection algorithms 
based on fractal dimension theory. Triangular method was 
chosen to obtain fractal dimension values of grids. The 
algorithm was not only effective and efficient to detect building 
edges, but also helpful for segmenting the building and nature 
objects. Future work was also discussed in the end.