Automatic Line Simplification Algorithm Selecting and Parameter Setting Based on Case-Based Reasoning

Currently, there are few researches on human-machine collaboration mechanism in the process of using line simplification algorithm tools. The simplification algorithm selecting and parameter setting depend on manual repeated correction, which reduces the usability of the algorithm. To solve this problem, we propose a case-based reasoning method for automatic line simplification algorithm selecting and parameter setting. Under the reference of the case, the computer performs case-based analogical reasoning on the candidate parameter sets through the similarity evaluation index and parameter optimization strategy, and automatically selects the best combination of line simplification algorithm and parameter in the same region and scale, without the cartography's continuous trial and error procedure. In the experiment, this method is used to distinguish the results of three linear simplification algorithms D-P(Douglas-Peucker) algorithm, Li-Openshaw algorithm and Bend Group algorithm, and automatically select the optimal algorithm and threshold. The experiment shows that the algorithm and parameters can be optimized automatically by this method, and the results of simplification are in good match with the existing data. It can effectively improve the efficiency and accuracy of the parameter setting and reduce the difficulty of using the simplification algorithm tool.