Jiawei Du, Fang Wu, J. Yin, Chengyi Liu, Xianyong Gong
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引用次数: 9
Abstract
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.
期刊介绍:
Cartography and Geographic Information Science (CaGIS) is the official publication of the Cartography and Geographic Information Society (CaGIS), a member organization of the American Congress on Surveying and Mapping (ACSM). The Cartography and Geographic Information Society supports research, education, and practices that improve the understanding, creation, analysis, and use of maps and geographic information. The society serves as a forum for the exchange of original concepts, techniques, approaches, and experiences by those who design, implement, and use geospatial technologies through the publication of authoritative articles and international papers.