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.

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the concept of classifying point cloud by using deep learning, specifically in the indoor environment. This is done by first doing a neural network comparison and then doing a case study. In the neural network comparison, a look is taken into which of the neural networks that are capable of working with point clouds is best suited for our experiments in the indoor scene, based on the training speed, accuracy, ease of use concerning training on external datasets and setting up the network and space efficiency. After the comparison, we chose to continue with the PointCNN network during the case study. The case study is performed on data the NS (Nederlandse Spoorwegen) provided to us and all test results we got from our experiments can be visualized using the web application we developed along with this project. The purpose of the case study is to add extra value to the indoor LiDAR point cloud the NS has captured from Amersfoort Station by using deep learning to automatically classify assets present in their data. The value is in purposes, such as asset management, where the data does not need possibly hundreds of man-hours to be labelled. This saves a lot of time and also money each time a scan is made. In the case study we found through 4 different experiments that unbalanced data makes for bad results, but when a scene is labelled correctly very good results can be found in a local scene.