ISPRS, Vo!.34, Part 2W2, "Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
several autonomous systems (Lockemann et al., 
1993). The main features of federated database 
systems are heterogeneity and autonomy. Autonomy 
means that a local databases management systems 
of a federated database systems should not be 
modified by the federated database systems and has 
the right to decide which types of internal information 
can be provided to the FDBS and to execute queries 
and transactions according to its own rules ( Sheth, 
A.P, 1990). A federated database can accept 
different heterogeneous systems and integrate them 
into higher level systems. They can be divided 
between two basic forms of interaction (Lockemann 
et al., 1993): 
•Cooperation. Cooperation means several 
systems or components use the same common data 
source. 
•Coordination. In case of coordination, the 
desired data will be copied amongJhese systems or 
components. 
Fig. 2 Cooperation vs. Coordination 
Interoperability refers in general to the ability of 
various autonomous systems to bring together parts 
and to operate in collaboration (ANDREJ 
VCKOVSKI, 1998). In most cases this means the 
exchange of meaningful information. Interoperability 
has the following levels or types: 
•Platform interoperability. It resolves the 
differences in the hardware, system software, and 
the services that deal with communication between 
two objects ( Lockemann et al., 1997). 
•Basic interoperability. It means that different 
binary components that are developed by different 
developers can interoperate each other. 
•Versioning interoperability. When a 
component is upgraded, all other system 
components need not to be upgraded. 
•Language interoperability. It is means that 
components that are developed using different 
languages can also communicate with each other. 
•Semantic interoperability. It resolves the 
communication between systems that are different in 
the definition of categories, different in the definition 
of the intension of classes and different in the 
geometric description ( Bishr, 1997 ). 
3. The heterogeneous Spatial Data 
Spatial data include many kinds of 
heterogeneous data. Vector, DEM and image will be 
described here. Remote sensing is an important 
source for vector data. It is able to cover large 
regions and can provide temporally continuous 
measurements. But most remote sensing systems 
can not handle vectors and are unsuitable for 
operations like network analysis. 
On the other hand, vector data is the auxiliary 
information of remote sensing image processing and 
classification, which is used to automatically extract 
semantic information. Thus the integration of remote 
sensing data with vector data is very important for the 
development of spatial information science. 
Vector data structures represent spatial phenomenon 
using continuous coordinate. The spatial 
relationships among map entities are stored or are 
computed when needed. 
DEM is used to model, analyse and display 
phenomena about topography or other surfaces. It 
has two major data structures: rectangular grid (or 
elevation matrix) and TIN (Triangulated Irregular 
Network). Girds present a matrix structure which 
records topological relation between data points. 
Storage structure of digital computers (i.e. grid) can 
be stored as a two-dimensional array of elevations. 
TIN structures, on the other hand, are based on 
triangular elements, with vertices at the sample 
points. 
Image data is stored as raster data structure. Raster 
data structures tessellate space and assign each 
spatial element a unique value. 
Fig. 3 The Integration of VectorDDEM and Orthoimage