As the way we interact with maps keeps changing, so do the maps change alongside. And it can be easily pointed out how these changes come alongside a large number of advantages for the average map user, such as quick access to data or the ability to view more or less of the Earth's surface with just a mouse scroll, as well as for specialists such as cartographers or spatial data analysts, as it is now easier then ever to manipulate complex data. That being said, the challenges have also shifted, from the expertise of the map maker to the software solutions which now do all the work.

One of the many challenges imposed by the aforementioned is represented by the way the map generalization process is achieved. This graduation project serves as a continuation to the countless amount of research which has already been performed in this field, with a focus on the niche world on Vario-Scale Maps, and in particular how borders are handled in this generalization process.

There is already a large number of different solutions available, some of them being considered as standard and used by some of the biggest players in the world of geo-information. However, it seems that no single one solution is a `silver bullet`, as they all have their advantages and disadvantages, as well as cases where one generalization workflow is clearly more suited then others.

Considering the actual status quo of the industry, this thesis will take a look at some of these already available solutions on the market, both individually as well as together, and will try to answer the following research question: \emph{To what extent can multiple line-generalization algorithms be (simultaneously) introduced in the Vario-Scale structure such that they preserve the topology and enable an optimal line density (while trying to preserve the characteristics of the initial shape as well).}

To reach an answer, it is necessary to start first from the lowest level, with understanding how line generalization function in different situations, then slowly building up the structure by introducing these new concepts in the broader workflow, to see what impact it has on it as a whole.