Using artificial neural network for labelling polygon features in topographic maps

Abstract

The purpose of this article was to present the methodology which enables automatic map labelling. This topic is particularly important in the context of the ongoing research into the full automation of visualization process of spatial data stored in the currently used topographic databases (e.g. OpenStreetMap, Vector Map Level 2, etc.). To carry out this task, the artificial neural network (multilayer perceptron) was used. The Vector Map Level 2 was used as a test database. The data for neural network learning (the reference label localization) was obtained from the military topographic map at scale 1 : 50 000. In the article, the method of applying artificial neural networks to the map labelling is presented. Detailed research was carried out on the basis of labels from the feature class “built-up area”. The results of the analyses revealed that it is possible to use the artificial intelligence computational methods to automate the process of placing labels on maps. The results showed that 65% of the labels were put on the topographic map in the same place as in the case of the labelling which was done manually by a cartographer. The obtained results can contribute both to the enhancement of the quality of cartographic visualization (e.g. in geoportals) and the partial elimination of the human factor in this process. Highlights for public administration, management and planning: • Map label placement is among key variables ensuring the usability of topographic maps across disciplines. • We present the neural network approach for automating the process of labelling topographic maps with locality names. • The presented case study applies to the military map in scale 1:50 000, but can be applied on other maps and geoportals.

Streamgraph Generation with Enhanced Readability

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Applied GIS in the context of smart regions and cities

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