Abstract
Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method.
Highlights
The cartographic label placement problem is a classical issue of map production, and is a combinatorial optimization problem [1]
Given that the label placement is a fundamental problem in map production and graphical information systems, the cartographic community has tried to propose cartographical rules to guide the process of label placement, which aim to generate high-quality results and productions especially for automatic cartography maps
We present an approach to the problem of multiple geographical feature label placement (MGFLP) within a unified framework
Summary
The cartographic label placement problem is a classical issue of map production, and is a combinatorial optimization problem [1]. While the above methods achieve promising results for the label placement problem, less attention has been given to line and area features. In the work by Wolff et al [17], the curvature of the lines along which the labels are placed is bounded from above by the curvature of a circle with radius r Their algorithm has been proved by an experiment showing that it runs in sub-quadratic time and generally yields good results in practice. Zhang and Harrie [25] presented a method that combines text and icon label placement in a real-time manner, which is achieved by labeling point and line features, and attached an icon to each of the area features randomly. While some methods do exist for joint label placement for point, line and area features, a major concern is the limitation of their algorithms in searching for the global optimum to fit the cartographical rules. The differential evolution is a global search algorithm based on tahbeilpitoyputolalteioanrnoffarosmet oinf pdoivsisdibulaelsoplouptiuolnasti(o“ncshraonmdosiosmaedsa”p).tiIvtehains ththeeabsielaitrychtoinlegardnirferoctmioinnd[2iv6i]d. uBayl pcoonpturloaltliionngsthaendseiasracdhadpitrievcetiionntohfetsheeardcihffienrgendtiiraelcmtiounta[t2io6]n.,BthyecDonEtraolglloinrigththme csaenarycihelddirbeectttieornroesfuthltes d[2i7ff]e.rTehnitsiahl ymburitdaitzioanti,otnhecoDmEbainlgeosrtihthemstrceanngythiesldofbbeotttehrGreAsualntsd[2D7D].ET,hwishhicyhbernidaibzlaetsioannceofmfecbtiinveesatnhde setfrfeicnigetnhtssooflubtoiothnGoAf thanedMDGDFEL,Pwphriochbleenmabalsesdaenmeoffnescttriavteedanind ethffiecrieesnut lstoslouftioounroefxtpheerMimGeFnLtsP. problem as demonstrated in the results of our experiments
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