A progressive method for the collapse of river representation considering geographical characteristics

During map generalization,the collapse of geometry,which is also called geometric dimension reduction,is a basic generalization operation.When the map scale decreases,rivers with long,shallow polygonal shapes,usually require their dual-line representation to be collapsed to a single line.This study presents a new algorithm called superpixel river collapse(SURC)to convert dual-line rivers to single-line rivers based on raster data.In this method,dual-line rivers are first segmented at different levels of detail using a superpixel method called simple linear iterative clustering.Then,by connecting the edge midpoints and centre of mass of each superpixel,single-line rivers are preliminarily generated from dual-line rivers.Finally,an interpolation algorithm called polynomial approximation with an exponential kernel is applied to maintain the uniform distribution of the feature points of single-line rivers at different levels of detail(LOD).The presented method can progressively collapse the river during scale transformation to support the LOD representation in a highly sensitive way.The results show that compared with three typical thinning algorithms,the SURC method can generate smooth single-line rivers from dual-line rivers considering different river widths while effectively avoiding burrs and fractured intersections.

A raster-based typification method for multiscale visualization of building features considering distribution patterns

In map multiscale visualization,typification is the process of replacing original objects,such as buildings,using a smaller number of objects while maintaining initial geometrical and distribution characteristics.During the past few decades,many vector-based methods for building typification have been developed,whereas raster-based methods have received less attention.In this paper,a new method for the typification of buildings with different distribution patterns called superpixel building typification(SUBT)is developed based on raster data.Using this method,buildings with different distribution patterns,such as linear,grid and irregular patterns,are first grouped by image connected component detection and superpixel analysis.Then,the new positions for building typification are determined by superpixel resegmentation.Finally,a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel.To test the proposed SUBT method,buildings from both cities and countrysides in China are applied to perform typification.The experimental results show that the proposed SUBT method can realize typification for buildings with linear,grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings.