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 
  
Figure 1. the first is elevation map; 2-9 are layers with 
different height. 
There are some empty holes in each connection areas SZ j 
(shown in Figure 1) One kind is that building's or 
vegetation's "top" and ground in space is not connecting to 
each other. These empty holes of the corresponding position 
can be eliminated. The second kind of empty holes are caused 
by the terrain. In the large connected region some small 
higher areas may be exist. These areas will produce empty 
holes and these empty holes should be retained. The 
difference between the tow kinds empty holes is showed in 
the figure 2. 
  
(a) (b) 
Figure 2. (a) The empty holes caused by building or 
vegetation. (b) The empty hole caused by small higher area. 
In each layer there are some of the lesser connection areas 
which are caused by two reasons. One is the height diffence 
between buildings (vegetation) and ground. The other is the 
local small low-lying ground. 
First of all we clear the noise of the original data. Then the 
data is stratified according to height. Value of layer-thickness 
should not be too large, otherwise accuracy is low. This paper 
choose 1 meter as a layer-thickness. And then began to merge 
gradually from the lowest layer, making judgment of the layer 
connection areas in the process of the merger .If there are 
empty holes, making judgment, delete empty holes caused by 
building and vegetation. As shown in Figure 3, we get these 
terrain points. 
  
  
  
  
    
  
   
   
  
  
Figure 3. (a) LiDAR data 
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Figure 3. (b) terrain points 
3. AUTOMATIC EXTRACTION OF ROAD 
The local geometric feature is implementing to filtering larger 
square, parking lot around road, etc. As in the local area the 
intensity on the road is similar, so adjusting to smaller 
regional range contributes to filtering out the parking lot, 
square and road. 
First suppose the topological relationships of road that: 
l. all roads are connected area with low intensity in 
local region. 
2. two different material road can not parallel and 
appear closely. 
Based on above two assumptions we can get the rough road 
outline. Because the road is connection areas with a low 
intensity, therefore we can get lower intensity connection 
areas, and determine whether it has the geometrical 
characteristics of the road. With topological characteristics we 
can rule out none-road area. Region growing is used to extend 
the road profile, and make up breakpoint caused by uneven 
local intensity. 
There are two steps to extract road. First local hierarchical 
geometric feature filter none-road area and get rough road 
outline. Second make road growing based on the road 
framework produced in the first step, and according to the 
connectivity make up small range breakpoints caused by 
uneven regional intensity. 
3.1 Local Hierarchical Geometric Feature Filtering 
LiDAR data is stratified by the intensity. For this purpose we 
describe the layer of the LIDAR data as Si, , 
Si, 2 (p; €S:jxAs<p,, <(jxAs+A))} à 
where Si j denotes the j-th layer , Pipi is the intensity of 
the last pulse of DJ is the number of the layer , As is 
the step length and. AZ is the thickness of the layer. Figure 4 
shows different layers with different intensity. 
order to get more accurate results and reduce the 
omplexity, this article deals with a smaller local scope. In the 
cal scope increase intensity value step by step, and judge 
onnection areas within the local region. When these 
onnected areas are greater than a certain threshold, judge 
hose shape and topological position respectively for deciding 
E Kcep or not. And then make judgment in next local small 
egion. Finally we get rough road profile.