Fully automated generalization of a 1:50k map from 1:10k data

Abstract

This article presents research that implements a fully automated workflow to generalize a 1:50k map from 1:10k data. This is the first time that a complete topographic map has been generalized without any human interaction. More noteworthy is that the resulting map is good enough to replace the existing map. Specifications for the automated process were established as part of this research. Replication of the existing map was not the aim, because feasibility of automated generalization is better when compliance with traditional generalizations rules is loosened and alternate approaches are acceptable. Indeed, users valued the currency and relevancy of geographical information more than complying with all existing cartographic guidelines. The development of the workflow thus started with the creation of a test map with automated generalization operations. The reason for the test map was to show what is technologically possible and to refine the results based on iterative users’ evaluation. The generalization operations (200 in total) containing the relevant algorithms and parameter values were developed and implemented in one model. Particular effort was made to enrich the source data in order to improve the results. The model is context aware which means it is able to apply different algorithms or adjust parameter values in accordance with a specific area. The result of the research is a fully automated generalization workflow that produces a countrywide map at scale 1:50k from 1:10k data in 50 hours. A fully automated workflow may be the only way to produce flexible and on-demand products; consequently, the results were implemented as a new production line in 2013. Issues for further research have been identified.

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