ABSTRACT
The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles.
Acknowledgments
Special thanks are extended to colleagues and reviewers for their constructive comments and valuable suggestions that substantially improved our manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Experimental results and trained ANNs in the experiment section that support this study are available on figshare at http://doi.org/10.6084/m9.figshare.13332914.