The needs and interests for 3D data based on large-scale topography are increasing. A wide variety of these 3D data needs have emerged in multiple domains and for many different applications. Though, research have shown that the interoperability of this 3D data nowadays still is a challenging task. Many barriers are caused by different perspectives of organisations, technical issues during conversions of data and a lack of guidelines. For this research, a case study is done in The Netherlands, one of the countries that experimented a lot with topographical data already. The Dutch Cadastre, Kadaster, has been working on a country wide covering 3D topographical basemap. However, also some of the Dutch source holders of large-scale topography have taken steps towards the development of 3D city models. The question that now arises, and also the main research question of this thesis, is: “How can a variety of 3D city models be integrated in a country wide covering 3D basemap based on large-scale topography?”. In order to achieve an answer to this question, the methodology has been split up into three components. The first component contained a literature study on 3D city models. The second component of the methodology included the interaction with stakeholders, such as Kadaster and various source holders of the Dutch topographical data. This interaction is performed by means of interviews and surveys. The third component contained the technical part, in which different 3D test data is collected and compared. The test data is for almost all stakeholders provided in CityGML, which is an international 3D standard used for 3D models. Various differences in the 3D data of the stakeholders are found. These differences can be found in the contents, the source data, the process and the management. Based on these test data and their differences, a workflow is developed in order to integrate the data. This workflow uses open source tools as cjio and citygml4j for the manipulation, integration and conversion of the data. This resulted in an integrated 3D model, containing both the data from Kadaster as well as the data from the source holders. Results have shown that the differences between the test data, semantically as well as geometrically, led to gaps and height differences in the final integrated model. This study has proven that different 3D city models can be integrated in a country wide covering model, which can be converted to various formats (CityGML, CityJSON and OBJ). A workflow is developed that integrates the test data of 5 Dutch source holders with the data from Kadaster. A few challenges during the conversion and integration of data had to be overcome. These challenges were either caused by errors in the code of the test data or by bugs and errors in the software tools. In the end, two proposals were given for further organisational developments towards a national 3D basemap based on large-scale topographical data. These proposals were based on the results of the literature study, the interviews with stakeholders and the data comparison and integration. In option 1, a national 3D basemap, developed and managed by Kadaster, is proposed. In option 2, a new basis registration, the 3D BGT, is proposed. In this situation Kadaster will provide a 3D basemap once and the source holders will collect and parse the mutations in 3D.

Point clouds are becoming one of the most common ways to represent geographical data. The scale of acquisition of point clouds is growing steadily. However, point clouds are often very large in storage size and require computationally intensive operations. The integration of point clouds nowadays still face a lot of challenges. This project focuses on one of these challenges; integrating point clouds of different scales and granularity. Solving this challenge enables appealing visualisation, usability for low and high computation powers and geometrical consistency for analysis. The following question is researched: 'To what extent can a vario-scale approach improve integration of point clouds with varying point densities?'. A data model is created that uses importance as an additional dimension. This dimension contains an importance value which is calculated using two methods. Firstly random assignment of values to the points and secondly exact computed values. To compute this value the smallest distances to its nearest neighbour is assigned as importance value. A web application shows the results. Both random and exact methods show an exponential decay in distribution of the importance value. Though the random methods run much faster, the exact methods preserve much more edges and other details.