This study explores the application of advanced photogrammetry techniques to enhance the accuracy of Digital Elevation Models (DEMs) derived from satellite imagery. It focuses on refining and integrating the photogrammetry pipeline to accommodate diverse satellite sources effectively. With recent advancements in photogrammetry and the expanding accessibility of satellite imagery, there is a growing opportunity for precise earth surface modeling. This research adapts a traditional photogrammetry pipeline to include state-of-the-art computer vision algorithms, specifically the DISK algorithm for feature detection and LightGlue for feature matching. These enhancements are complemented by tailored adjustments to camera models and projection matrices to suit the unique characteristics of satellite data.

The methodology emphasizes the modification of existing pipelines to optimize the handling of satellite images, incorporating sophisticated feature detection and matching technologies. The performance of these adaptations is rigorously evaluated through extensive analysis using satellite imagery across varied resolutions and environmental conditions. Results from the study indicate marked improvements in the fidelity and accuracy of the generated DEMs, which are substantiated by validation against high-resolution LiDAR ground truth data.

The refined pipeline effectively manages multi-source satellite images and produces terrain models of significantly higher quality, vital for robust geospatial analysis. This work not only bridges the gap between remote sensing and computer vision but also lays the groundwork for future research aimed at improving DEM generation from satellite imagery. This study proposes potential transformative practices in geospatial analysis and supports continued progress in fields such as environmental monitoring, urban planning, and disaster management.

Accurate positioning has become an essential component of modern life, crucial for applications ranging from navigation and industrial operations to emergency response. The Global Navigation Satellite System (GNSS) has traditionally provided reliable positioning, but its effectiveness diminishes in environments where satellite signals are obstructed, such as dense urban areas and indoor spaces. This thesis explores the potential of Fifth-Generation (5G) wireless communication technology, specifically utilizing Received Signal Strength Indicator (RSSI) data for positioning, as an alternative to GNSS. The research investigates the effectiveness of 5G positioning through trilateration and compares it with GNSS-Real-Time Kinematic (RTK) positioning. The study aims to validate the accuracy and the reliability of 5G positioning and various real-world scenarios, focusing on challenging environments. Key aspects examined include the impact of topography on positioning accuracy and the influence of network distribution on Position Dilution of Precision (PDOP). By attaching a 5G modem to a laptop, field measurements were collected and analyzed against the "ground truth" provided by GNSS-RTK. The results demonstrate the potential of 5G RSSI-based positioning to serve as a robust positioning solution. This study’s findings hold significant relevance for the geomatics community, with implications for urban planning, infrastructure development, environmental monitoring, and disaster management. Through critical analysis and validation, this thesis contributes to the advancement of positioning technologies, highlighting the limitations of 5G trilateration using RSSI, yet proposing it as a potential complement to GNSS. The findings pave the way for future research and practical applications in enhancing precise positioning systems.

The kinds of physical spaces present in the real world are becoming ever more complex, and the locations defining the boundaries between these spaces are often arbitrary. Distinguishing between which spaces count as `outdoors,' and which count as `indoors,' becomes more difficult when `semi-outdoor' and `semi-indoor' spaces are considered. Integrating these different spaces within geovisualisations is difficult because data on the spaces are often collected and stored separately.
Many existing navigational applications avoid the explicit differentiation between different types of spaces, or choose to only visualise one type of space.
Additionally, these applications rarely identify which areas are visible to users at their present positions, and which areas are occluded.
This thesis explores the potential of utilising point clouds directly in geovisualisations to communicate information about the types of spaces surrounding a hypothetical user in a real-world environment.

Raw point cloud data is collected on three different transitional spaces, all of which contain an outdoor element. These point clouds are classified into four different `space-types' (outdoor, indoor, semi-indoor, and semi-outdoor), and visibility analysis is performed on them directly. The resulting information on space-type and visibility is combined within multiple different data visualisations, the concepts of which have been designed using a list of requirements based on existing literature.
The visualisations show that there is potential for direct use of point clouds in communicating information about spaces to a user, and that discerning between visible and occluded spaces, has potential value to a user orienting themselves within their environment with aid of a geovisualisation.

Nowadays, the military stakeholders take advantage of the various geospatial infrastructure and technology while exploiting the wide use and distribution of geographic data, to model military scenes and/or conduct geospatial analysis for military operational scenarios. While a host of technology is offered, in potential joint military operations or joint civil-military operations, several difficulties elapse in geospatial communication, due to different Coordinate Reference Systems (CRS), non-conforming resources, different data formats and different areas of responsibility perception between the stakeholders. The multidisciplinary geospatial communication, data integration and a common modelling geospatial framework is integral for the accurate and fruitful modelling of a military scene assisting in joint operations. Discrete Global Grid Systems (DGGS), while not being a new concept, recently emerged in the geospatial community with various distributed implementations, as a framework to integrate, analyze and manage geospatial data. This thesis attempts to apply a DGGS, using an existing distributed implementation, to integrate different format, two dimensional and elevation geospatial datasets, coming from military and civilian stakeholders, to identify the potential of this approach for military scene modelling. The different approaches of quantization of the datasets are explored for their efficiency, quality and the ability to assist in performing geospatial military analysis, while according APIs are developed for the DGGS conversion. Given the model of the military scene the research tackles the geovisualisation alternatives and a case study is also conducted for a military geospatial operation (ranging) using the DGGS approach. The results show the promising potential of the DGGS approach to model military scenes, providing a uniform area based framework, encapsulating the different qualitative and quantitative military information, offering strong connectivity and hierarchy relations and increasing the qualitative spatial perception and multidisciplinary geospatial communication. The datasets’ DGGS conversion showed advanced integration, segmentation, aggregation and visualization capabilities, also exploiting the 3rd dimension data. Uniform storage through the DGGS integration can be realized in a database, facilitating the distribution of military data. A DGGS can also support common military geospatial operations (i.e. ranging). However, the quality of the results is highly dependent of the original data accuracy/spatial uncertainty and the demanded precision requirements of the scene’s coverage. In parallel, due to the fact that this technology is still emerging and dynamically optimized, it is not mature yet to handle high precision requirements and be treated as a frame with high positioning accuracy for high scale military scene coverages.

In the modern world, evacuating a building in a safe and orderly manner remains a challenge. Fire-based emergencies in a building are dynamic environments that can be simulated to better understand, analyse, and contribute to safer and smarter buildings. While different spatial representations exist, voxels provide a structured, flexible and efficient 3-dimensional grid for applications like analysis, classification, surface reconstruction and simulation. Furthermore, voxels implicitly contain topological and spatial relations that are relevant for 3-dimensional events such as evacuations in a rapidly changing building, with a fire spanning multiple floors.

Voxels can suffer from the problem of scale and resolution, where high resolution voxel scenes take up a lot of memory space. For this, there are solutions that make more efficient data storage for voxels possible. These include but are not limited to: the regular voxel grid, the sparse voxel octree, directed acyclic graphs and the use of space filling curves. Finding the shortest safe path for the evacuees is a challenge, especially if the area is dynamic, and there are other actors that have to share the space. Many different pathfinding algorithms exist, each with their own speciality, such as A*, any-angle pathfinding algorithms like Theta* and incremental algorithms like D*-Lite. 

In this thesis, we look at whether voxelised indoor spaces can form the basis for evacuation simulations with multiple actors in a dynamic situation. We do this by comparing both the voxel data structure and pathfinding algorithm combinations in a dynamic evacuation simulation application. The comparison is done by looking at the quality of the paths, if the algorithms are able to adapt to a dynamic situation and the performance of the paths, both in computation times and memory load.

These experiments reveal that a time-aware variant of A* is able to outperform the other algorithms, when applied on a sparse Morton grid. Additionally, it shows that the use of a sparse Morton grid is preferable to implementing a full octree or the use of a non-sparse regular voxel grid for dynamic multi-actor voxel scenes. Finally, the experiments show that dynamic events can be added into pathfinding algorithms by separating walking the path from finding the path, and using a data structure that is time-aware.

Mapping an environment with a Light Detection and Ranging (LiDAR) sensor through the use of a LiDAR Simultaneous Localization And Mapping (SLAM) algorithm is a powerful technology that allows for the creation of detailed 3D models. Recently various LiDAR sensors have been developed based on Micro-Electro-Mechanical System (MEMS) technology. These LiDARs are very low cost and considerably smaller than conventional LiDARs. They also often incorporate other sensors such as Inertial Measurement Unit (IMU)s and cameras into the same device.
Performing LiDAR SLAM with MEMS based LiDAR is challenging due to the short range, the smaller Field of View (FOV) and the sensitivity to ambient light of MEMS based LiDAR. In this thesis the objective is to reduce the effect of these factors when doing LiDAR SLAM by incorporating IMU measurements into the position estimation of the sensor.
A graph based positioning approach is proposed to achieve tight coupling of the IMU sensor and LiDAR position estimates. The method is made more robust by incorporating an outlier detection mechanism that reduces the influence of wrong LiDAR position estimates caused by insufficient points in the LiDAR FOV or by ambient light disturbance.
The method was built in ROS and implemented on the Intel ® L515 sensor. The performance is evaluated in indoor situations with varying presence of ambient sunlight and where room size approaches the maximum limit of the sensor range. The algorithm achieves lower drift than the current state of the art for the Intel ® L515. The algorithm especially achieves altitude drift reduction and increases robustness to outliers in the LiDAR positioning.

The merging of multiple partial maps of indoor environments created
by teams of human or robot agents into a single global map is a key problem
that, when solved, can improve mapping speed and quality. Existing map
merging approaches generally depend on external signals which are not available indoors or only use the geometric properties of an environment. Inspired by the human understanding of environments in relationship to their context we propose a map merging system that extracts and uses topometric maps, a map representation containing both the geometric and topological characteristics of an environments, to solve the map merging problem in indoor spaces. In this research we demonstrate an intuitive approach to extracting topometric maps of 3D, multi-floor, indoor environments and use both the topological and geometric characteristics contained in the topometric maps to perform context-aware map matching and fusion.

As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.

The Deployment of Indoor Point Clouds for Firefighting Strategy project was realised as a Synthesis Project of the Geomatics Master Programme of the Built Environment Faculty at the Technical University of Delft. This project was executed by a team of five Master students in collaboration with the Dutch response team collective Veiligheidsregio Rotterdam-Rijnmond. The objective of this project is to develop an information system that makes use of indoor data to support tactical decision-making during fire emergency responses. The main challenge that response teams are facing when they develop deployment plans is the lack of appropriate information about indoor spaces. As a result, response teams may end up relying on inaccurate assumptions which can lead to dangerous situations. New technologies such as SLAM devices and augmented reality displays, combined with processing techniques, can be used to supply them with the information needed to make the right choices. The result of this project is a prototypical information system containing an interactive, 3D environment that can receive updates, merge data from different data sources, and accommodate mixed reality information sharing in real-time.

In 2017 the Netherlands had 7146km of railways [Ramaekers et al., 2009], which are owned and managed by ProRail and have to be frequently surveyed, for quality inspection. Surveying is done using a variety of surveying techniques, many of which require surveyors to walk on or near the tracks, which could cause injuries. An alternative surveying technique would be photogrammetry using images acquired with a camera mounted on a drone. In this technique a 3D point cloud is constructed out of images, which is georeferenced using GPS measurements on the drone and Ground Control Points (GCP). The positions of the rail rods can then be found by modelling the rail rod using the point cloud. The quality of the point cloud, defined here as a combination of accuracy, precision, point density, completeness, and the rail rod feature detectability, is an important factor in deciding if photogrammetry is a sufficient surveying technique for this project.

In this research the influence of the distance between the camera and the track, the baseline, and the number and spread of GCPs on the quality of the resulting point cloud. The distance to the track was set to 25, 30 and35m and the baseline was varied between 2 and 10m. The requirement set for the accuracy is 15mm, while the precision has to be below 10mm. For the point density an K-nearest neighbors (K-nn) of at least 10 is required, the completeness requires less than 10% missing cells and finally the feature detectability requires that at least once every 2m the location of the rail can be determined. The features are detected using a 2D model of the rail that is fitted in a flattened slice of the point cloud using a modified version of the iterative closest point algorithm.

It is shown that the accuracy and precision are influenced by the distance and baseline, but no clear relation was found, while the accuracy improves when using GCPs. For the point density it is shown that increasing the distance between the camera and the track linearly decreases the point density, while the baseline has no impact and for the completeness a decreasing trend was found when increasing the baseline, while no relation could be found between the completeness and the distance between the camera and the track. For the rail rod feature detectability it was shown that the percentage of accurately found rail rod positions goes down when increasing the distance to the track and the baseline. Overall it is proven that when using a 100MP camera, 5 GCPs, a distance of 25m, and a baseline of 2.17m the rail rods could be surveyed so that the requirements set for this research were met.

Because of urbanisation, more than 50% of the global population now lives in cities, a number that is ever-increasing. This leads to a need for more dense and complex building constructions, driven by the fact that people are living increasingly close to one another. These structures are often so large that it becomes difficult to navigate in the environment without any assistance. Especially for people with restricted mobility, such as wheelchair users, or the blind or partially sighted, it can pose a challenge to find the best path in a building. Consequently there is a need for navigation graphs that provide a way to structure connectivity information and routes in an indoor environment. Existing methods generally obtain information on the location of various rooms, corridors and their interconnectivity from 2D floor plans or 3D building models. However, these plans and models often were created by the architect when the building was planned, but never updated with new information after the building was built and came into use. Manually updating them is very labour-intensive. Therefore, in this research a method is developed for obtaining a navigation graph from an indoor environment by automatically extracting the information needed from a point cloud. The navigation graph is modelled according to the Open Geospatial Consortium (OGC) standard IndoorGML, which provides a basic structure for indoor navigation. The point clouds used in this research are gathered with a hand-held Mobile Laser Scanner (MLS), which also saves the path (trajectory) taken in the building. This all leads to the main research question: How can a navigation network in IndoorGML format automatically be extracted from a cluttered indoor point cloud and its trajectory? In order to answer this question this research focuses on the detection of doorways, and how they connect indoor spaces, such as rooms and corridors. A new way of door detection is proposed, which is based on the identification of walls using the 3D Medial Axis Transform (MAT) of the point cloud. After this it is explored how the established connectivity relationships can be extended with accessibility information, such as the location of stairs, and the dimensions of doors. Additionally it is researched how a more detailed navigation graph can be created by subdividing large spaces that have multiple connections. In this thesis it is proven that geometric input for a navigation graph can automatically be obtained from a point cloud. 100% of doors could be detected in the dataset that the methods were developed on, and when testing the methods on another point cloud, this number decreased to 70% , due to unforeseen situations. All spaces connected by detected doors, stairways and sloped surfaces were included in the graph, from which a more extensive navigation graph could be produced by subdividing corridors. This final product was compared to networks drawn by humans on a corresponding floor plan, from which it can be concluded that the graph produced by the methodology of this thesis is a good basis for a navigation.

When the re brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. In this thesis we contribute to an ultrasonic sound sensor for human presence detection in smoke-lled spaces. This type of sensor could assist the re brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research an ultrasonic sensor was built for less than 20 Euros and it was found to be unaected by the glycerine based smoke that it was tested in. Using a particle filter based on sequential importance resampling as well as a Filter based on Gaussian approximation of the posterior density the resulting system was reliably able to detect when there was a single person walking in the sensor direction, even when other sources of movement such as doors and chairs were present.

Every day, terabytes of information is generated, filling storage devices around the world. However,the human brain have limited capacities to read and understand raw data from a computer screen.That is why data specialists need to ingeniously create better ways to display, process and analyzemassive amounts of data.Our research project is not about avoiding subsidence, not even about cracks on buildings; it ispurely data analysis and interpretation. This study will help professionals understand and fightagainst building subsidence. Our task was to create, manipulate and make sense of charts like theone below (a real line graph from InSAR data), then translate them into useful information forstakeholders in the local, national and global community.The aim of the project was to understand if ground sensor technologies are comparable to othersources of information. In our analysis different strategies to analyze building subsidence wereimplemented, e.g. homogeneous subsidence, heterogeneous subsidence and for water levels,interpolation and cross correlation methods. In addition, other techniques like sensor fusing wereimplemented to compare data from different sources.As a result from all these strategies, we can say that the water level sensors placed in our researchbuilding, have a high similarity with citizens and municipality data. In contrast, InSAR data is notcomparable with the subsidence sensors placed in the building because they have differentreferences and the period of study was too short to get accurate results from satellite data. Finally,an idea for future implementation strategies was proposed. On this idea, measurements of levelscan be carried out taking as a reference the NAP level and comparing the subsidence between ahealthy-foundations building and another one with wooden-piles foundation.

From this study can be concluded that using drones equipped with cameras a 3D model van be created of an incident on the rail. From the recordings, a 3D model van be made using photogrammetry. In this model it is possible to measure post distances with an estimated error of less than 1 centimeter. The distances between the laser scanner point cloud and the point cloud created with the drone are in 78% of cases smaller or equal to two centimeters. In order to further reduce the distances between the point clouds more photos from the bottom of the train.