An approach to data fusion using uncertain knowledge in geographical information systems
Makoto Suzuki, Dai Araki, Akira Higashide, Teruaki Suzuki · Electrical Engineering in Japan · 1999
Digital terrain maps have become widespread, and Geographical Information Systems (GIS) have moved into the limelight. One of the key technologies needed in GIS is data fusion reasoning. The function of data fusion is to consider various geographical data, such as “roads” and “slope,” and make a complete evaluation. In this paper, we propose a data fusion reasoning technology using uncertain knowledge. Data fusion knowledge contains some uncertainty. For example, our knowledge for evaluating mobility costs is uncertain because it is qualitative, such as “we want to refuse steep places.” We introduced two uncertainty reasoning mechanisms to represent such data fusion process. One is fuzzy reasoning, and the other is Dempster–Shafer theory. We also offer knowledge-editing facilities for describing data fusion knowledge, such as a data flow diagram editor for designing data fusion process and a membership function editor for describing data abstraction methods. These knowledge editors facilitate the development and modification of data fusion knowledge base for GIS. © 1999 Scripta Technica, Electr Eng Jpn, 128(4): 65–76, 1999