Point feature adjusting algorithm in digital map based on multi-evaluation factors

The same real world objects are often repeatedly gathered by different departments, which may lead to data ambiguity. To resolve it, adjusting is a key step which is defined as making different representation of matching objects consistent. Previous methods adjust one map directly without considering the influence caused by the point features from the second map, which certainly brings some error. To solve this problem, the point feature adjusting algorithm based on multi-evaluation factors is studied in this paper. Three primary evaluation factors are analyzed, and their importance is given by the method of entropy, then the adjusted position is obtained with a weighted average algorithm. Finally some same point features from a sea chart and a topographic map are utilized to test the proposed algorithm. The result shows that it can improve the quality of point feature adjusting, make them close to the position of the real-world entities and has wide-application.