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Further the primary data analysis should give an 
accuracy rating for the achievable terrain description 
out of a quality test and also an estimation of realistic 
parameters (mesh width) needed for the final DTM 
generation. 
Within an other tool a homogenous point distribution 
can be generated to a certain extent by the interpolation 
of artificial points in areas without any information. By 
means of these artificial points an insufficient terrain 
description can be avoided for those critical areas. 
5. REALIZATION OF COMPONENTS FOR 
PRIMARY DATA PREPROCESSING 
5.1 Checking primary data 
Primary data of large DTM's usually are submitted in 
several data files. The completeness of this data files for 
the whole DTM area has to be checked in a first step. 
This task can be done with tools for the presentation of 
data on a graphical screen or a plotter. In addition the 
range of the data in all three dimensions is tested to get 
the parameters for the data base area or to find points 
that are clearly out of range. Program tools for the 
detection of identical points or crossing lines have to be 
used as well. 
5.2 Data management 
Organizing primary data is important for program tools 
that need fast and uniform access to the data of large 
DTM's. These requirements are valid for programs 
deriving DTM follow-up products as well as for pro- 
grams used to check and refine the primary data. 
A DTM data base such as the HIFI data base (Ebner 
et.al,1988) is able to manage large DTM areas. The 
primary data can be stored in a HIFI data base. For the 
data preparation steps as described in the following 
chapters it is necessary to build a DTM structure that 
can be updated easily. Using a preliminary DTM with 
the structure of a triangulated irregular network (TIN) 
powerful capabilities for updating data and DTM 
structure are available (Reinhardt, 1991). Both this TIN 
structure and the combined data structure of HIFI can 
be used within the GIS Interface of HIFI (Ebner et.al., 
1990). Therefore this GIS Interface, consisting of 
interface subroutines for data handling, data editing and 
product derivation, is suited best to organize the primary 
data for data preparation purposes. Since the DTM of 
  
852 
the GIS Interface is handled within the main memory of 
the computer, data processing can take place inter- 
actively but is limited by the maximum number of points 
that can be stored within the main memory DTM (e.g. 
8000 points for a main memory DTM with the size of one 
megabyte). The data of large DTM areas can be loaded 
patchwise from the HIFI data base into the main 
memory DTM and stored back into the HIFI data base 
after data correction and refinement. 
53 Quality test and gross error detection 
After structuring the data by a TIN the surface of this 
preliminary DTM can be used to estimate the data 
quality of the examined area as well as to detect gross 
errors. One algorithm for both purposes was developed 
and realized as a new subroutine of the GIS interface. 
The idea of the algorithm is to calculate the deviation 
between the surface represented by the TIN and the 
surface of the continuous terrain (see figure 2). 
Terrain 
TIN 
Deviations 
Figure 2: Deviations between TIN and 
continuous terrain 
The amount of this deviation, called surface deviation 
value (SDV), can be used to assess the quality of terrain 
description by the primary data. As the surface of the 
continuous terrain is unknown, an assumption for the 
surrounding surface of a point is necessary. This surface 
can be described with the normal vector at the point and 
a constant curve assumed around the point. The normal 
vector of the specific point is calculated with a weighted 
mean value of the normal vectors of each surrounding 
triangle. Using a weight depending on the covered sector 
is important to consider the influence of the different 
triangles. A plane, set up by the normal vector of the 
triangle and the connection between the measured point 
and the main point of the triangle, is used for a simplified 
handling of the SDV computing (see figure 3). Now it is 
possible to calculate the constant curve defining a circle 
and the SDV for each triangle using the normal vector