originally in map form, including ground truth, soils maps, 
meteorological and topographic maps. The data base system is of 
vector type with coupled attributes. RDS provides a small, but 
flexible set of features (Kiss, 1988) that are generally expected 
in a vector based geographic information system (GIS). 
Most geographic information systems, despite of their immense map 
creation and data base maintenance capabilities, fail to 
substantially support multidimensional, multisource analysis and 
modelling (Marble et al, 1983). That is why a separate subsystem 
for geographic data modelling subsystem (GDMS) assumed appropriate 
to be developed. This division of the common GIS functions into 
two groups supports image analysis and modelling better (Bryant et 
al, 1976) when different data sources are involved creating a many 
dimensional problem of the parameters being spatially distributed. 
To emphasize the concept and to make a clear distinction between 
digital map handling and modelling using multidimensional spatial 
data, the term geographic information and modelling system (GIMS = 
RDS + GDMS) seems to be more appropriate. 
Thus the IAS and GIMS are planned to be in a fairly symmetric 
relation. One direction when IAS gives result maps etc. to the 
GIMS is common. The way and rate how much the IAS makes use of 
GIMS data is quite different in the existing processing systems. 
In our tasks we try to emphasize this latter, as some features in 
the practice of the Hungarian agriculture enable us to do that. 
However it might worth considering its application in different 
environments, too. 
The system controller is now analysis steps oriented instead of 
representing an expert system terminal. The data base consists of 
raster, vector and attributes types of data that arises the 
problem of vector—raster conversion overhead. On the other hand, 
storing ancillary data in original vector form from which one can 
derive an adequate raster system (cell size) ensures the necessary 
resolution and the required accuracy at the same time. 
4. SUMMARY OF CROP SURVEY PROJECTS USING GIS 
GIS support can be used at different levels in crop survey and 
remotely sensed data analysis. The following brief summary 
enhances the real advantage of the more effective GIS supported 
image analysis methods. 
Conventional crop survey methods using weak GIS/GIMS support 
Though fairly good results were achieved in crop mapping on 
smaller areas (Csornai et al, 1983) for inventories and crop 
surveys on larger areas (0.5—2 million hectares) the possible 
highest level of automation is inevitable. In a pilot crop survey 
project for a rather complex county, Hajdu—Bihar (approx. 600.000 
hectares, Fig.2) GIS support was restricted to the stratification, 
training and test (Csornai et al , 1988).