Abstract
The performance of a line simplification algorithm can be different depending on the shape characteristics of the line data. Methodologies for segmenting a line feature into homogeneous sections and simplifying the segments with a proper algorithm are of great importance in linear generalization. This study proposes a methodology for segmenting and simplifying linear features based on the quantitative characteristics of a line. We analyzed the performance of existing simplification algorithms based on the geometrical attributes of line shapes. The analyzed data was used as a criterion for segmenting line data and selecting simplification algorithms appropriate for each segment. Then, we implemented segmentation and hybrid line simplification on topographic map data with a 1:1000 scale. To evaluate the performance of this methodology, visual and statistical assessments were implemented. As a result, this hybrid approach preserves more of the shape characteristics and produces less positional errors than the individual application of existing algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.