2004 
  
atabase 
EIETEIEE 5g 
/ienna, 
Urban 
of the 
ization 
Urban 
ves of 
Part3A, 
h geo- 
\SI354, 
omatics 
Vol.28 
us and 
ospatial 
GEO-INFORMATION MANAGEMENT 
i Dana Klime&sová 
Czech University of Agriculture, Prague 
Faculty of Economics and Management, Dept. of Information Engineering 
Kamycka 129, 165 21 Praha 6 — Suchdol, Czech Republic 
klimesova@pef.czu.cz 
and 
Czech Academy of Sciences 
Institute of Information Theory and Automation, Dept. of Image Processing 
Pod vodarenskou vézi 4, 182 00 Prague 8, Czech Republic 
klimes(@utia.cas.cz 
Commission IV, WG IV/1 
      
KEY WORDS: Geo-information management, contextual modelling, hierarchical identification, spatial-temporal analysis, group 
hierarchy, fuzzy design. 
ABSTRACT: 
The contribution deals with the contextual design of spatial data for the purposes of the regional development, land management and 
government, describes the role of remote sensing data and contextual modelling for the context oriented geo-information and tries to 
provide the framework for the object hierarchy of classes and propose the ways of structure and behaviour modelling. The digital 
geographic databases are rapidly growing and the problem has shifted from finding the data to finding meaningful geographic 
information or better knowledge from the large volumes of data. GIS technology is changing to GIM technology (Geographical 
Information Management). Many users have their own specific requirements for the access to data, with regards to the administrative 
units where the evaluation is provided and considered the relations and contextual and temporal background for the data processing. 
Object-oriented image analysis is based on an object-oriented approach to image analysis. In contrast to the classical image 
processing method, the basic processing units are image objects or segments. Further motivation for the object-oriented approach is 
the fact that the expected result of most image analysis tasks is a set of real world objects. The topological relations of single or 
adjacent pixels are given by the raster implicitly. The increasing resolution of the sources results in the increasing number of objects 
of course and moreover the complexity of object structuring hierarchy is rapidly growing too. The automation of the classification 
and interpretation process gives better results when the fuzzy elements are implemented. 
by the fact that it corresponds very closely to our understanding 
1. INTRODUCTION of the around world. The same scheme is valid in case of the 
concept of time. 
1.1 Spatial Data Collection 
There is no life without the time consideration. There is no past 
It is evident now that the combination of spatial and temporal and no future. And the natural process connected with time 
components of information and incorporation of computational accounting is analysis of changes. 
intelligence into spatial data analysis will bring the new 
qualitative reasoning of geo-information. The progress in new 1.2 Temporal Analysis 
sensor technology for Earth observational remote sensing 
continues and increasingly high spectral resolution multispectral Automated process of evaluation of temporal data sets is called 
imaging sensors are developed and these sensors give more — temporal analysis. We need to know the history to be able 
detailed and complex data for each picture element. understand the trends and model our future. The management of 
any thing, including acceptation of the important decisions, it is 
The increasing resolution of the sources results in the increasing not possible to do having the inventory information. To be 
number of imaged objects (classes), increase the dimensionality successful in management of things it means to account the 
of data, and moreover, the complexity of object structuring changes, understand the trends and effectively plan the changes 
hierarchy is rapidly growing too. if possible. 
In addition, the geo-society calls for temporal data, temporal The temporal context model characterizes the processes of 
oriented spatial databases. This need comes from natural contextual coding, storage and retrieval. To retrieve context, we 
necessity of registration of changes appearing around us on one 
: : : FT 
Te E introduce an item-to-context matrix M that connects the F 
hand and the need of monitoring of long-term processes and ; He 
trends and theirs interrelations on the other one (Klime&ová, layer (space) to 7’layer (Howard M. W., Kahana M., 2001). The 
2001). The great development of geographical databases and input is obtained by presenting current stimulus to the matrix 
GIS technology oriented on spatial data at all has been caused M. 
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