Precise and comprehensive knowledge about 3D urban space, critical infrastructures, and below ground features is required for simulation and analysis in the fields of urban and environmental planning, city administration and disaster management. In order to facilitate these applications, geo-information about functional, semantic, and topographic aspects of urban features, their mutual dependencies and relations are needed. Substantial work has been done in the modeling and representation of above ground features in the context of 3D city modelling by means of CityGML. However, the below ground part of the real world, of which utility networks form a big part, is often neglected in 3D city models. At the same time, several existing utility network data models exist. These are, however, commonly tailored to a specific domain and not suitable for the integrated modeling and representation of utility networks and city objects in 3D urban space

This research proposes a 3D data modeling approach for integrated management of below ground utility networks features (viz. electricity and sewer) and related above ground city objects (viz. streetlights and manhole covers). The data modeling approach is successfully examined by implementing relationships between 1) the below ground electricity network and above ground streetlights and 2) between the sewer network and the above ground manhole covers.

Having existing utility network data and city objects as input, a file in CityGML Utility Network ADE format is created. The manipulation of the data structure and content, according the proposed data modeling approach, is completed in FME. The output CityGML dataset allows interoperability but may become very large and objects may be arbitrarily nested leading to complex data structures. Therefore carefully optimized database schemas are required that enables efficient storage, management and data access of the CityGML data. The object-oriented CityGML data model, including the Utility Network ADE, is mapped to a relational database by means of the 3DCityDB. Subsequently, the CityGML data is inserted into the derived relational database. Several relevant (network) analyses are performed by querying the designed relational database. It shows the possibility to simulate what network features are affected by e.g. a utility strike by means of pgRouting and visualization in a GIS.

This research made one of the first attempts to thoroughly model existing utility network data and city objects according the CityGML Utility Network ADE. Following are further research that could optimize the proposed data modeling approach for better decision making in the field of asset management:

- Modeling multiple different utility networks and city objects
- Modeling in a higher LoD
- Detailing the CityGML Utility Network ADE classes and use
- Better investigating on more types of analyses
- Implementing larger datasets
- Implementing datasets with a different accuracy
- Exporting a CityGML file from the relational database
- Better investigating on visualization of the data
- Investigating on how to model different types of relationships

Navigation from a room inside a building to another room inside a building which is across the street consists of three parts: first, the indoor part at the building where you start your journey. Secondly, the outdoor part and thirdly, another indoor part inside the building of destination. As regards to the outdoor environment, a navigation aid is well used and implemented in many types of applications. However, it is not common to use an aid to navigate inside a building. While such an indoor navigation aid is not necessary in small buildings, it is a necessity in more complex buildings like hospitals, airports, conference venues and large shopping malls. The indoor navigation aid can help visitors finding their way inside these, for them, unknown locations. The systems behind a navigation aid consist of several elements like an indoor positioning system, an indoor navigable map, specific destinations (points of interest) and an appropriate guidance throughout a building. This research focusses on the creation of indoor navigable maps that can be displayed and used to plan possible routes throughout the entire building. The indoor environment is far more complex than the outdoor environment. First, most people lose their orientation inside a building after they change their direction several times. Second, since there are no pre-existing routes inside a building, there are many different possible ways to arrive at a destination. Third, there is a large variety of associated spaces which all have their own unique interior design. Therefore, automating the process of making an indoor map is more challenging and time consuming than generating an outdoor map.

Most research in the field of automatic generation of indoor maps is focused on the already available 2D floorplans and only few of them use the more complex 3D representations. Using a 2D floorplans for the purpose of indoor navigation has its limitations because of various factors. Firstly, the 2D maps are already a simplification of the complex 3D environment which can lead to difficulties in representation. Secondly, the connectivity between different floor plans can be difficult as each floor plan is a separate entity. Thirdly, the maps that are available do not always contain furniture. Fourthly, in addition to the third one, most existing methods only focus on reconstructing the indoor space as an empty hull which results in a navigation aid which has does not emphasize the attention to obstacle detection. At last, most floor plans are out of date because some buildings are not built according to their blue prints. Their interiors might change after several years through the modification of walls and doors and furniture may be repositioned to the users preferences. Therefore, information about the indoor environment must be updated in most cases. This research concentrates on the automatic generation of indoor navigable spaces for pedestrians based on laser scanning with a Mobile Laser Scanner (MLS) device. These devices scan the environment continuously along a trajectory which makes them more time efficient than terrestrial laser scanners.

To aid pedestrians in their indoor navigation, features needed for path computation such as floors, stairs, walls and furniture elements, need to be identified. These must be extracted from the point cloud which is generated by the MLS. How to identify these elements, like walls and doors, is investigated a lot. These researches are built on a set of constraints, like a Manhattan World or a flat surface constraint. These constraints are not problematic for a regular office building but they will provide difficulties in more complex buildings. This means that it is important to focus on a method with less or without constraints.

Scanning the indoor environment of an building often happens during business hours which automatically leads to the inclusion of dynamic objects like pedestrians or small vehicles in the final point cloud. These dynamic elements do not represent any type of building elements (like furniture) and thus need to be identified and removed.

Beside the point cloud, the MLS also stores the trajectory of the MLS device. This trajectory contains three types of valuable information. The points directly below the trajectory indicate areas where pedestrians can walk, since the MLS device was operated by a pedestrian. The height difference between neighboring trajectory points can be used to detect stairs, slopes and flat surfaces and the trajectory also provides information about the connection of different surfaces and represents the complexity of the building.

In this research, a method for the identification of walkable surfaces based on the analysis of a point cloud and the corresponding trajectory of the MLS is developed. First, the point cloud is voxelized. Second, the trajectory is analysed to detect the three different types of navigation surfaces: stairs, slopes and horizontal surfaces. This classified trajectory is projected vertically on the voxel model to acquire seed voxels. These seed voxels are then used to create areas by using region growing. These areas can be modified by identifying dynamic objects, entryways and furniture elements so that each area represents a specific navigable voxel space inside a building.

Experiments shows that by applying this method, it is possible to create a continuous navigable space in buildings including several floors, stairs and elevations. Data can be captured during opening hours because the method detects and removes dynamic objects in the final result. The proposed method can be used for any type of room without any constraints because the complexity of the building is already present in the trajectory of the MLS.

Humans interact more and more with their environment through technology, recent decades have seen a huge increase in the need for and availability of Location-Based Services (LBS). Recently landmarks have gotten a renewed interest in the field of LBS , although already a quite old phenomenon. In both the outdoor and indoor environment they are being used to enrich existing services such as navigation, but not used as the basis for a technique or service.

The indoor environment relies heavily on building specific and less-scalable sensor-based localisation techniques (such as Wi-Fi and Bluetooth), alternatives to sensor-based are becoming a necessity and would be a welcome addition. The exploration and development of landmark-based approaches for indoor localisation is something that can extend the field of geomatics and \ac{lbs}.
This research investigates if a pure landmark-based approach works for indoor localisation and which characteristics of landmarks can be exploited. This is achieved by developing a conceptual framework that explores how a landmark-based indoor localisation would work from an artificial point of view. A Minimal Viable Product (MVP) is implemented to evaluate if a landmark-based approach works and what needs to be improved or considered in future studies

Starting from an artificial test case, the MVP to achieve indoor localisation is implementing and evaluated using a manually digitised real-world and more complex test case. The fundamental principle of landmark-based localisation is that through the observation of landmarks within the (indoor) environment a user’s location is obtained because the visibility and location of landmarks are known. The workflow to go from an observation to a location is by 1) calculating the visibility/isovist area of each landmark, 2) interpret the observations into a combination of landmarks, 3) intersect the visibility of all landmarks in the observation, 4) refine the location based on relative landmark constellations, and 5) follow-up with questions on potentially visible landmarks to improve location further

One of the key giveaways of this research is that approach for indoor localisation a landmark-based is feasible, principles and techniques exist (or are being developed), it is only a matter of setting them up in the right order and format them to work, and connect input with the researched process and use them for LBS driven applications.
Future work on the subject of landmark-based localisation and LBS is connecting with existing spatial standards, extend the principles into the 3^rd dimension, and integrate more aspects of landmark salience.