RJMCMC based Text Placement to Optimize Label Placement and Quantity

Abstract : Label placement is a tedious task in map design, and its automation has long been a goal for researchers in cartography, but also in computational geometry. Methods that search for an optimal or nearly optimal solution that satisfies a set of constraints, such as label overlapping, have been proposed in the literature. Most of these methods mainly focus on finding the optimal position for a given set of labels, but rarely allow the removal of labels as part of the optimization. This paper proposes to apply an optimization technique called Reversible-Jump Markov Chain Monte Carlo that enables to easily model the removal or addition during the optimization iterations. The method, quite preliminary for now, is tested on a real dataset, and the first results are encouraging.
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Guillaume Touya, Thibaud Chassin. RJMCMC based Text Placement to Optimize Label Placement and Quantity. Proceedings of the International Cartographic Conference, ICA, Jul 2017, Washington, DC, United States. ⟨10.5194/ica-proc-1-116-2018⟩. ⟨hal-02097492⟩

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