Figure 11: First pulse — last pulse model. Gray shading and grid 
width as in figure 10. 
These models are now used for a classification step. Figure 9 
shows a digital orthophoto of the area with the buildings of the 
research forest. Figure 10 shows difference models first-ground 
and last-ground, Figure 11 shows the model first-last. Now all 
areas that exhibit differences larger than one meter in the model 
first-last are assumed to be vegetation points. The threshold value 
of one meter was chosen because it corresponds to the grid width 
of one meter; buildings in Austria usually have roofs tilted less 
than 100%, thus the difference between first and last pulse 
models should not exceed 1 m. 
Areas with values lower than 1 m in the data set last-ground are 
assumed to be ground points which normally cannot be 
penetrated by the laser. The threshold of one meter here 
corresponds to the medium hillslope and ground vegetation. 
Those areas with values lower than 1 meter in the data set first- 
last and higher than 1 m in the data set last-ground are assumed to 
be building points. Figure 12 shows the resulting mask after 
applying a 3x3 despeckle filter. This mask can further be 
improved by expanding the building areas (black) to the area that 
shows values larger than 1 m in the data set last-ground (dense 
areas). The resulting mask is shown in Figure 13. 
The usage of aerial or satellite imagery may be of great help in 
distinguishing between vegetation an man-made objects. Yet, the 
proposed algorithm shows good results and works fully 
automatic. It could be used to automatically find those areas were 
there may be buildings (or rocks or other solid off-terrain 
features). Manual inspection could then allow to further 
distinguish between different object types. 
4 CONCLUSION 
Roads can be well extracted from laser scanner data of 
mountainous regions. For deriving complete road networks (e.g. 
for a GIS) a semi-automatic approach is advantegeous. The most 
    
  
  
   
  
   
  
  
  
  
    
  
   
   
   
   
   
   
  
   
  
  
  
  
  
  
  
    
   
  
   
  
  
   
   
   
   
   
    
   
    
    
   
    
International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999 
  
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Figure 12: Building mask. 
  
  
  
  
Figure 13: Building mask enlarged with first pulse model. 
significant road sides, which are extracted automatically, can be 
used to produce a geo-morphologically corrected DTM. Further 
investigations will concentrate on extending the concept for 
extracting general break lines (where only the direction of the 
slope changes abruptly). 
For buildings, the candidate regions can be detected fully 
automatically. Visual inspection is still necessary to distinguish 
buildings from large isolated trees and in order to assign 
additional attributes for classifying them in a GIS. In addition, the 
grid points inside the candidate regions have to be matched to 
geometric 3D-building models. 
Our results have shown the high potential of extracting spatial 
information from laser scanner data. 
   
Internation 
5 A( 
The research was fun 
the projects P1281: 
Research Program S7 
[Fuchs, 1995] Fuchs 
Course in Digital Ph 
the Institute of Photo; 
10, 1995 
[Kass et al, 1988] K: 
Snakes: active contou 
Vision, 1(4), pp. 321- 
[Kerschner, 1998] Ke 
integrated in a bundle 
Photogrammetry and 
Columbus, Ohio, 199 
[Kraus and Rieger, 
Processing of Laser S 
Spiller (Eds.), Photog 
Verlag, pp. 221-231.