Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty

Jingsheng Zhai,Hang Xie,Zhaoxing Li,Fang Wu,Bo Zou

doi:10.3390/ijgi6060184

Jingsheng Zhai, Hang Xie + Show 3 more

Open Access

https://doi.org/10.3390/ijgi6060184

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Abstract

This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment.

Highlights

As the most common and most important category of features in maps and geo-spatial databases and transmission, linear features and their generalization, especially simplification, have received considerable attention and have been widely studied
The degree of reduction in length means a loss of information on the linear feature, which is considered as a quality assessment index for linear feature simplification [32,33]
By regarding the linear feature as a whole, the length ratio method provides a kind of coarse-grained simplification method, whose advantages lie in its easy implementation, low computation cost and robustness

Summary

Introduction

As the most common and most important category of features in maps and geo-spatial databases and transmission, linear features and their generalization, especially simplification, have received considerable attention and have been widely studied. Most these methods are based upon one same hypothesis, i.e., that the original linear feature is accurate [2] An advantage of these methods is their simplicity and rather slight computation cost. This hypothesis affects the accuracy and reliability of the assessment results of these methods, and users and geographers will remain unclear about the extent to which the simplification process has changed the linear feature’s spatial uncertainty, not to mention the spatial uncertainty distribution of a certain location or point on the feature, which is useful information in both guiding geographers to conduct generalization and enabling users to make better use of map products

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