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One of the major contributions of geographical information systems (GIS) is their ability to efficiently manage geographical information. Geographical information can be represented and stored in a variety of ways, each with its advantages. Thus, a challenging problem facing GIS is their ability to effectively utilize the various types of geographic data. The process by which these different information sources are merged in order to yield a more comprehensive dataset is referred to as conflation. Currently, conflation is performed manually whereby a cartographer has to overlay two maps and painstakingly match geographical features. This arduous process is highly subjective and prone to error. Therefore, our objective in this research is to develop a methodology for automating this process. Our approach is to simulate the cognitive and intuitive processes that a cartographer uses in performing feature matching. Based upon observation, we have identified three criteria that a cartographer uses when performing this process. They are spatial, nonspatial, and topological. Thus, the major challenge is to develop innovative techniques whereby these characteristics can be utilized in automating the conflation process. In this research, we expect to address the issues concerning this problem and develop practical solutions as to how the problem can be solved. Our goal is to significantly improve the way this process is currently performed. Furthermore, we intend to perform extensive tests in order to substantiate our hypotheses.
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