Abstract
This paper presents a new method for use in performing continuous scale transformations of linear features using Simulated Annealing-Based Morphing (SABM). This study addresses two key problems in the continuous generalization of linear features by morphing, specifically the detection of characteristic points and correspondence matching. First, an algorithm that performs robust detection of characteristic points is developed that is based on the Constrained Delaunay Triangulation (CDT) model. Then, an optimal problem is defined and solved to associate the characteristic points between a coarser representation and a finer representation. The algorithm decomposes the input shapes into several pairs of corresponding segments and uses the simulated annealing algorithm to find the optimal matching. Simple straight-line trajectories are used to define the movements between corresponding points. The experimental results show that the SABM method can be used for continuous generalization and generates smooth, natural and visually pleasing linear features with gradient effects. In contrast to linear interpolation, the SABM method uses the simulated annealing technique to optimize the correspondence between characteristic points. Moreover, it avoids interior distortions within intermediate shapes and preserves the geographical characteristics of the input shapes.
Highlights
With the development of web mapping and big geo-data, there have been significant changes in the goals and methods of map generalization
Some new methods have emerged, such as continuous generalization, on-the-fly generalization and on-demand mapping [7,8,9].This study explores the problem of the continuous generalization of linear map features by shape morphing
Continuous generalization denotes the use of various generalization techniques in real time to generate geographic information at arbitrary scales with smooth and continuous changes
Summary
With the development of web mapping and big geo-data, there have been significant changes in the goals and methods of map generalization. Traditional Multi-Scale Databases (MSDBs) do not meet the demands of users for arbitrary scaling [6] To overcome these deficiencies, some new methods have emerged, such as continuous generalization, on-the-fly generalization and on-demand mapping [7,8,9].This study explores the problem of the continuous generalization of linear map features by shape morphing. The third solution achieves continuous generalization based on shape morphing by obtaining a map representation at a meaningful intermediate scale and interpolating between the two anchor scales [12,13,14,15,16].
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