Proceedings Article | 10 January 2005
KEYWORDS: Roads, Data modeling, Web services, Internet, Data integration, Geographic information systems, Internet technology, Remote sensing, Spatial resolution, Pattern recognition
Most spatial data organizations need automated conflation technology. For the same geographic area, an organization usually has several sources of spatial data, with each source differing in terms of available spatial features, attributes, resolution, accuracy, and other qualities. Processing multiple sources of spatial data, along with their respective differences and advantages, is a huge and ever increasing problem. The maintenance of spatial data is very costly and time consuming. This situation will become intensified when more and more digital spatial data are offered by using Internet technologies.In this paper the conflation technology for integration of spatial data from different source on the Internet is introduced. Conflation attempts to match the spatial data of the source and destination coverage. If the coverage have different origins, it is likely that the shapes, and even the locations of the features, do not match exactly. Most conflation algorithms only match similar features that are very close to each other. But indeed, spatial object is defined by its location, shape, attributes and relationship to others. Therefore, before the matching itself, the semantic relations, topological relations and geometrical matching technologies have to be probed. The research work is performed on road network, which are captured in different data models on the Internet. The approach is based on matching criterions between the spatial data of different data models. At first, the semantic relations have been considered, and then different data models compare with topological relations and select the similar data sets. The geometrical matching has been done in the selected data sets and chooses the best reasonable one for the conflation result. It can get more quickly speed than other conflation approach based on statistical investigations before.
