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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [4 references]  Display  Hide  Download

Contributor : Guillaume Touya <>
Submitted on : Friday, April 12, 2019 - 9:20:14 AM
Last modification on : Thursday, May 9, 2019 - 2:44:03 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



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⟩



Record views


Files downloads