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.

The seismic building structural type (SBST) reflects the main load-bearing structure of a building and therefore its behaviour under seismic load. For numerous areas in earthquake prone regions this information is outdated, unavailable, or simply not existent. Traditional methods to gather this information, such as building-by-building inspections, are costly and highly time-consuming, making them unfeasible for assessing large building inventory. For this reason, the use of remote sensing data has been proposed to allow a fast acquisition of relevant building information on urban and regional scale. Subsequently, machine learning algorithms may be used to analyse the gathered data, e.g. to classify a building stock into groups with similar seismic behaviour. This thesis investigates into such an approach for the building stock of Groningen in the Netherlands. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. We thereby follow the assumption that similar structural systems and materials can be inferred from geometric similarities. To describe the geometric shape of a building we apply Shape DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on a synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80%, only relying on the Shape DNA. The experiment shows promising results, however, further research is necessary in order to explore the usability on real building data. In a second experiment we use a sample of buildings from the Groningen building stock. Here we can automatically predict detailed SBST information, such as the type of lateral load resisting system, with accuracies above 80% only by taking a buildings footprint area and year of construction into account. (see thesis document for extended abstract)

With fast development of both hardware and software, 3D real-world scene data, for example multi view stereo mesh, can be acquired efficiently. And since 3D city data are more realistic and more useful in some applications, they begin to appear in the market. Although the generation of 3D city data like multi view stereo mesh can be fast, the data might contain many measurement errors. Because of these measurement errors, some regular objects such as building, ground etc sometimes are not exactly flat. Points which should be on the same plane have small deviations from the plane they belong to, which makes the flat surface bumpy. One way to solve this problem is to control the quality of data acquisition and data processing. Unfortunately, even the data error sources are known, it is not possible to eliminate all the errors, and accurate but expensive data acquisition equipment sometimes are not affordable. In that case, processing existing data is much more economic and time saving compared with collecting data again. This MSc thesis aims at solving above problem. It provides a methodology on straightening planar parts of multi view stereo mesh of the city. After straightening the mesh, this thesis also tries to simply the mesh by removing redundant vertices and faces in the mesh, so that the data will be clean and the data storage can be reduced.