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Structural information may be explicit or implicit. They 
denote meaning of the data elements, relations between 
them, and significance of relations. Such relations are first 
of all topological and in addition also morphological, 
hydrological or derived. 
Continuous functions are used for approximation of the 
modeled terrain surface. DTM is generally considered as 
a 2.5 D surface with only one elevation attribute. There 
are many possibilities for generating different functions, 
which are based on interpolation of structured data 
elements. 
Quality information depends on semantic perception of 
structure of the real Earth surface. This is the nominal 
ground or desired level that is tried to reach with the 
highest level quality of captured and modeled data. 
Methods for implicit functions analyses are partly 
connected with structural information data. But they are 
more generally connected with methods for analyses in 
GIS. 
Figure 1: Flowchart of the DTM / DEM 25 modeling. 
On the basis of this introduction the difference between 
DEM and DTM can be made. DEM includes only elevation 
data (look to data elements) that are generally not 
considered as terrain surface. In most cases DEM is grid 
data with elevation attributes, which is suitable to use for 
analyses in raster GIS. Term DTM includes more general 
information than DEM. DTM is a modeled surface 
structure which contains also other data of terrain as 
following: ridgelines, peak points etc. With simplification, 
the term DTM may be used in general. 
3. PREPARATION FOR INTERPOLATION 
3.1 Interpolation draft 
DTM interpolation from many data sources - as in our 
case study - demands many data preparing and 
managing steps. Main modeling steps are shown by 
flowchart at figure 1 and described bellow. 
3.2 Selection of test regions 
Relief morphology of Slovenia is quite heterogeneous and 
so it is not easy for terrain modeling. It can be roughly 
classified to alpine, karst, hilly and flat surface regions. 
Case study for DTM modeling bases on a test data which 
has been chosen with respect of the mentioned 
morphological classes. Test regions were optimized to 
have as much as possible relief characteristic on relative 
small areas. In the selected areas we were also trying to 
include relevant quantity of available input data with the 
elevation attribute. 
Figure 2: Test areas in Slovenia: Krsko (1), Alpe (2) and 
Kras (3). 
On figure 2 we can see three test areas for DTM 
modeling. The first (1) is hilly and flat surface which has 
dimension of 11,250 x 18,000 m. The other two are alpine 
-mountainous (2) and karst (3) regions with dimensions of 
4,500 x 3,000 m. 
4. DATA FOR DTM MODELLING 
4.1 Description of potential data 
We decided to use for case study only data that is 
available at Surveying and Mapping Authority of the 
Republic of Slovenia. The following are potential input 
data: 
1) raster data; 
digital elevation model with 100 m raster (DEM 
100),