Up-to-date 3D data is essential for urban planning, building inspections, and monitoring changes in the built environment. While 2D aerial imagery is widely used, it lacks height information and is sensitive to shadows and seasonal effects. In contrast, 3D point clouds provide detailed spatial information and enables better interpretation.

This thesis presents a method for detecting structural building changes using bitemporal airborne laser scanning (ALS) data from the national height model of the Netherlands (AHN) and the Rotterdam municipality. These datasets are pre-aligned in the stelsel van de rijksdriehoeksmeting (RD)-normaal Amsterdams peil (NAP) coordinate system and include building classifications, which allows the focus of this research to be placed directly on detecting change.

Comparing point clouds from different time epochs is challenging due to differences in density, noise, occlusion, and scan geometry. To address this, a random forest (RF)-based classifier is trained on synthetically generated urban scenes that simulate realistic change scenarios. These synthetic scenes are made with different scanning parameters, incorporating diversity in the training dataset. A certainty index is introduced that combines the model’s probability output with occlusion visibility across both epochs, providing a confidence measure for each prediction.

The method is applied to real AHN and Rotterdam datasets. Since no labelled ground truth is available, results are evaluated visually. The method successfully identifies structural changes such as dormers and extensions, and also detects moved or temporary objects such as sunshades or picnic tables. When combined with aerial imagery, the approach helps distinguish static from dynamic changes.

This work is innovative in its integration of occlusion-aware certainty scoring, visual certainty feedback, and the automated generation of synthetic training data for change detection

This thesis explores the development and validation of a synthetic framework for oblique aerial image adjustment and object point detection, with the goal of improving photogrammetric workflows in complex urban environments. The research is motivated by the inherent challenges of oblique imagery, such as occlusion, perspective distortion, and variable visibility, which complicate traditional adjustment procedures. To address these issues, the study employs a novel approach by generating synthetic test cases that emulate real-world oblique aerial data, enabling controlled experiments and sensitivity analyses. Utilizing data from recent aerial campaigns over Rotterdam, including both nadir and oblique images, the research implements and evaluates various adjustment and feature detection algorithms, including Bundle, DISK, SIFT, and LightGlue. The synthetic framework allows systematic testing of key parameters and environmental conditions, such as occlusion and lighting variations, providing insights into the robustness and limitations of different methods. Although the results demonstrate promising potential for synthetic data to replicate key geometric and photogrammetric behaviors, challenges remain in achieving full photorealism and seamless transferability to real-world applications. The findings underscore the importance of synthetic data in advancing urban geospatial systems and support the early-stage design of aerial collection systems, with particular relevance for municipal maintenance, planning, and infrastructure management in the Netherlands. The study concludes with recommendations for future research directions, emphasizing the integration of more photorealistic synthetic imagery and improved synthetic-to-real transfer methods to enhance the accuracy and reliability of oblique aerial mapping workflows. Overall, this work contributes to the growing body of knowledge on synthetic data use in photogrammetry and opens pathways for more resilient and efficient urban mapping solutions.

This thesis proposes a structured and scalable workflow for semantically enriched Smart Point Cloud (SPC) grounded in Heritage Building Information Model (HBIM) ontology. Rather than representing the heritage object with vector-based parametric models, this approach treats the smart point cloud itself as a valid HBIM geometry representation, preserving geometric fidelity while attaching multi-layered semantic information at the patch level. A structured semantic model is defined through a literature-based ontology review, encompassing structural, material, historical, cultural, and conservation-related characteristics. The SPC workflow is implemented and tested on two heritage case studies: the Herdenkingsmonument Kartuizerklooster and the Aula of TU Delft. Each case demonstrates the generality of the method under different geometric and semantic complexities. The semantic annotations are stored externally in structured JSON files, ensuring modularity, version control, and future interoperability. A lightweight web-based viewer was developed using Three.js to support interactive visualization and interpretation, enabling users to explore structure, material, and cultural information directly in the browser. Although full integration with 3D Gaussian Splatting (3DGS) could not be achieved due to current toolchain limitations, the thesis outlines strategies for propagating patch-level semantics to 3DGS centers, as well as segmenting and visualizing per patch with Gaussian Splatting, establishing groundwork for future research in full semantically integrated rendering. Overall, this study contributes a reproducible methodology for documenting, interpreting, and disseminating heritage datasets in a way that aligns with HBIM objectives while minimizing modeling overhead. The data processing and the visualization platform are shared on Github by https://github.com/Zhuoyuee/thesis and https://github.com/Zhuoyuee/spc viewer/tree/main.

Building heights are important information for a variety of subjects, such as wind analysis, energy demand simulations and solar potential assessment, yet large-scale LiDAR scanning is costly. This thesis introduces PHYSHADE: a set of physics-guided U-Net-based models. It employs shadow projections derived from building footprints and solar geometry into an aerial image shadow segmentation pipeline, for the purposes of building shadow extraction and consequently the large-scale estimation of building heights. Thirty-five aerial images in the Netherlands were manually annotated for buildings and their associated buildings. By employing transfer learning, based on a general purpose shadow-segmentation model, a total of 130 models were trained, which can be categorized into three different implementations of PHYSHADE. Through these different configurations, the performance impact of the various methods of addition of pseudo-shadows to the models was ablated. Afterwards, the best-performing PHYSHADE configurations were used with a raycasting algorithm to convert shadow lengths and solar altitudes back into building heights. The inclusion of pseudo-shadows lifted the mean Dice scores from 0.53 to 0.85, with an average gain of 0.32 and statistical significance across different folds. Physics-guided loss, based on the pseudo-shadows, was not found to be significantly different in most cases, whilst hurting model performance in some cases compared to the pseudo-shadow enabled models. On six out-of-fold test tiles the best PHYSHADE variants retained Dice scores of 0.72 – 0.95, although recall declined in one winter scene. Finally, height estimation on these tiles using the inference from the best PHYSHADE variants resulted in mean RMSE of ≅ 1.9m and MAE of ≅ 1.5m. While its application needs to be tested in broader contexts, PHYSHADE offers a viable low-cost complement to LiDAR for building height estimation.

High-resolution image mosaicking plays a critical role in geomatics and remote sensing
applications, allowing efficient visualization, measurement, and analysis of large-scale envi
ronments. Although existing commercial tools provide standard stitching capabilities, they
often lack mathematical transparency and real-time customization, limiting their utility in
research and professional analysis.
This thesis introduces a systematic approach to dynamic image stitching and visualization
within a C# environment. The method uses homography transformations to achieve ac
curate image alignment while integrating an optimal seam-finding algorithm to improve
visual coherence in overlapping regions. An exportable homography matrix supports co
ordinate traceability, enabling users to perform metric evaluations on stitched images. The
implementation focuses on creating a lightweight, interactive stitching prototype capable of
processing two to three aerial images with high geometric fidelity and run-time efficiency.
Experimental validation confirms that the system delivers precise stitching results and sup
ports visual exploration for measurement tasks. By combining mathematical clarity, dy
namic responsiveness, and user adaptability, this research contributes to a modular and
extensible foundation for image mosaicking in the context of geomatics, with practical rele
vance for aerial inspection, photogrammetry, and spatial data visualization

In the summer of 2023, heatwaves became quite prominent in the south of Europe. Due to the extreme heat, the health of those citizens was affected. The Netherlands Meteorological Institute predicts an increase in heatwaves in the future for the Netherlands as well. The main research question is how to propose a strategy for a liveable environment by designing public spaces while mitigating heat stress for vulnerable target groups in the context of Bospolder Tussendijken in Rotterdam. This research questions also how a reproducible tool could help identify heat stress and test design interventions in Dutch cities. The research included a literature review, expert consultations, scenario planning, modelling of the urban environment and mapping techniques.
Comparing the heat stress software reproducibility, computation time, possibility to test design interventions and the scale of modelling were important. Improvements in the reproducibility of the PET map of Koopmans et al. (2020) are made by creating an open-accessible QGIS plugin applicable to Dutch cities. This helps urban designers to indicate and test their design interventions. Refinement of the wind calculation contributed to speeding up calculation times of the wind for neighbourhood and city scale areas. Future research should focus on some refinement in PET calibration to work properly, and advanced wind modelling is required for urban design practices.
The application in the Rotterdam test case study emphasizes the importance of maintaining liveability now and in the future. By enhancing social liveability and physical liveability within a network of heat-mitigating interventions liveability is guaranteed. By revealing the vulnerable groups and their social interactions on a summer day, the most frequently used routes are qualified for refurbishment. Based on the current quality of social space and walkable environment, ownership and degree of open space on the street level, the interventions are chosen for the situation.
The research emphasized the importance of identifying heat stress in public spaces and the need for urgent action to maintain the quality of life in the future. By integrating informed strategies from multiple fields like Geomatics and Urbanism a climate-adaptive and healthy environment can take shape.

In recent years, the need for heritage preservation and reconstruction has become evi-
dent as many mature buildings face the risk of deterioration, damage or loss due to fac-
tors such as urban development, environmental weathering as well as outdated infras-
tructure. This urgency has created surges of significant interest to find sustainable meth-
ods of heritage preservation. The rise of emerging digital technologies has introduced
a multitude of innovative methods for storing, analysing, and showcasing building data.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable de-
tailed documentation and virtual exploration of heritage sites, while digital databases and
archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in con-
junction with segmentation methods to create a visually accurate model while also incor-
porating semantic labels.

This study explores the effectiveness of Virtual Reality (VR) compared to the use of 2D interfaces in interpreting point cloud data, focusing on user perception, interaction and relative measurement accuracy. Visualizing point clouds is often challenging due to the limitations in translating three-dimensional data into two-dimensional screens. VR offers a potential solution to enhance depth perception and deepen user understanding. The research utilizes Omnibase, a platform developed by Geodelta, that integrates various spatial data types, including point clouds, for applications such as municipal boundary measurements.

The study involved participants that are either familiar or unfamiliar with point clouds, to evaluate VR versus Omnibase. Quantitative measurements and qualitative feedback were collected on either platform. Results indicate that while VR provides better depth perception and a more immersive experience, it presents a steeper learning curve, especially for inexperienced users, additionally, it comes with physical side effects. The measurements in Omnibase showed higher consistency, though not necessarily greater accuracy, due to depth misinterpretations.

In addition to the study, the VR testing environment was developed using Potree.

This project presents an indoor navigation system based on image matching, aiming to address the challenges of localization and navigation in indoor environments. The system utilizes Simultaneous Localization and Mapping (SLAM) technology to capture high-resolution images and point cloud data, combined with the VGG16 model from Convolutional Neural Networks (CNN) for image processing, feature extraction, and matching.

In our research, we conducted experiments at the Faculty of Architecture and the Built Environment of Delft University of Technology, using a SLAM scanner to obtain 360-degree panoramic images and point cloud data of the indoor environment. Through cube mapping projection, we converted the panoramic images into six planar views, selecting the front, right, and left views as positioning references. Additionally, we reconstructed the indoor environment structure and designed node networks for positioning and navigation.

The technical architecture of this system comprises three main components: VGG16-based image feature extraction, cosine similarity-based image matching, and DBSCAN algorithm for location clustering. Through this method, the system can achieve real-time localization results after image capture and provide users with optimal paths using the A* navigation algorithm.

Experimental results show that when using single image matching, the system's room localization accuracy reaches 74.65\%. When employing multiple image matching and DBSCAN clustering methods, the accuracy significantly improves. In our final evaluation involving 116 positions, the system successfully matched 111 of these positions to their correct rooms, achieving a localization accuracy of 95.69\%.

This research not only provides an innovative solution for indoor positioning and navigation but also points the way for future research, including support for multi-floor navigation, enhancing CNN model performance, and automating building processing. This technology has the potential for widespread application in complex indoor environments such as large buildings, conference centers, and university campuses, offering users accurate, real-time positioning and navigation services.

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.

Global Navigation Satellite System is a spatial data acquisition technique, mostly used in navigation and positioning. One of the main components of this technique is the satellite visibility, which refers to the connection between the satellite and the ground receiver. It is known that the GNSS positioning systems are not as performing in urban areas due to the dense coverage of obstacles (buildings, trees, high terrain etc.). These obstacles can obstruct and reflect the lines of sight between the satellite and the ground receiver which can affect the quality of the performance of the GNSS service. The geometry configuration of the satellites above the receiver is another important aspect that has to be taken into consideration.

This research focuses on implementing a simulation similar to that of GNSS mission planning tools, but using point cloud data as the 3D representation of the surroundings of the receiver and using only the GPS constellation of satellites. Due to the large size of a point cloud sample, two visibility algorithms have been implemented to filter the necessary 3D data. The main output of the simulation are the dilution of precision values which give further information about the satellites' positions. The main purpose of this research is to understand the dilution of precision values, which are directly related to the geometry of the satellite configuration above the receiver. Understanding the behaviour and how the receiver's environment influences the DoP values can result in leading GNSS surveying missions with better results.

This output is then compared with the data acquired from a GNSS receiver in a real scenario. While the results are not favorable for the implemented simulation, it gives a better understanding of the surroundings of the receiver's location by using point cloud data than the already existing online GNSS tools.

Water management is an integral part of Dutch history, driven by the continuous need to reduce flood risk. Because a large area of the country is located below Normaal Amsterdams Peil (NAP), there is an ongoing challenge to safely discharge all the water to the sea. Therefore, flood safety policy has become crucial to protect the Netherlands from natural hazards. An essential part of this strategy involves the Waardegedreven Onderhoudscontract Uiterwaarden (WOCU) Rijntakken project, which is responsible for managing the floodplains adjacent to the Rijntakken within the Netherlands.

The current lack of efficiency and effectiveness regarding change inspection in the large and sometimes inaccessible areas of the floodplain requires the use of remote sensing change detection to move toward a data-driven maintenance process, in particular, by using point cloud data. This is nowadays a widely used data source in a variety of fields to capture elevations and in this way extract valuable information from terrains. Despite its usage in a variety of applications, the data is often underused since the data is frequently processed directly to other data formats. This research therefore aims to reveal the potential of explorative point clouds in floodplain maintenance.

Light Detection and Ranging (LiDAR)- and multispectral data were acquired at two moments, one before and one after the summer, with a time interval of 45 days. Subsequently, these acquired datasets evolved into an explorative point cloud by adding attributes, including vegetation health, also known as Normalized Difference Vegetation Index (NDVI), and the distance between these two point clouds, the cloud-to-cloud distance. This explorative point cloud with the integrated additional information was visualised to several disciplines involved in the WOCU project. This was done in Three Dimensional (3D) by using Virtual Reality (VR). This collaborative approach revealed the potential use cases of the Red, Green, Blue (RGB), cloud-to-cloud distance, and NDVI point clouds highlighting the potential of explorative point clouds.

Potential use cases that were found are; highly detailed area modeling, vegetation overgrowth monitoring, bank erosion detection, flora status assessment, monitoring of vegetation types, digital inspection of remote sites, participation medium, and identification of atrophied ground patches. Attributes added to point clouds enhanced insights. Especially the RGB point cloud sparked excitement due to its realistic appearance. The Cloud-to-Cloud Distance (C2CD) attribute showed potential, especially for erosion detection. However, due to the short timeframe between measurements, it could not be detected. The NDVI attribute was perceived as less interesting.

The use of explorative point clouds, generated from raw LiDAR point cloud data, offers potential uses and insights for floodplain maintenance. The interdisciplinary value of explorative point clouds was clearly visible. This thesis emphasizes that underused raw LiDAR data, by making it explorative, can act as a valuable resource.

The NMCAs (National Mapping and Cadastral Agencies) of European countries have different cadastral survey accuracy standards (European Global Navigation Satellite Systems Agency, 2019). In order to meet these standards, the appropriate equipment and services should be determined. The augmentation service Galileo High Accuracy Service (HAS), that is planned for 2022, will provide high accuracy Precise Point Positioning (PPP) corrections. Unlike other high-accuracy services, the Galileo HAS will be free of charge and available worldwide, without the need to be close to a base station or to a dedicated provider network. The PPP corrections will be provided through the Galileo signal as well as through the Internet (EUSPA, 2021). Because of the potential of the Galileo HAS, for the Synthesis Project we want to get insight in the accuracy of the augmentation service. Since a big share of cadastral surveys is performed in the built environment, we also want to determine the accuracy in an urban canyon. With the found accuracy, we can possibly judge whether Galileo HAS is suitable for cadastral surveys in the Netherlands, by comparing the measured accuracy to cadastral survey accuracy standards of the Dutch Kadaster.
As a final conclusion for this project, Galileo HAS is still a technique under development and the PPP-based correction methods are currently not as accurate as the RTK-based ones. Galileo HAS will present in the future ways to correct these errors.

The declining availability of practical hours for medical anatomical education has prompted Enatom to develop a digital anatomical platform, utilizing the open-source WebGL-based point cloud renderer Potree. This platform, which employs detailed point cloud scans of anatomical structures, aims to offer a dynamic and interactive educational experience. Although Enatom's focus is not directly on geomatics, the techniques employed in this project have strong parallels with those used in geomatics, thereby enabling a symbiotic exchange of expertise. This interdisciplinary approach enhances the development of Enatom’s digital platform, with the potential to contribute to the field of geomatics. To address existing user experience challenges, this project has added a lasso-selection tool tailored for Potree, advanced annotation capabilities, and methods for sensitive data anonymization within the point cloud. The project's outcomes will be available in an open-source format at https://github.com/GEO1101-Synthesis-Group4/Selection-Annotation-Repo. This project exemplifies the versatile application of geomatics expertise beyond its traditional scope, demonstrating its potential in enhancing diverse domains such as medical education.

The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the Dutch National Archive. This was based on the  corner coordinates of the map sheets. The first issue that had to be tackled was the reprojection of the original coordinates which were in Bonne projection
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence.

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 evolution of localisation and navigation technologies is vast, aiding towards facilitating users’ guidance in various environments. Outdoor positioning can be easily achieved, with the widely used Global Navigation Satellite Systems (GNSS), which comprise a universal standard for positioning and are included in every person’s mobile device. However, due to the presence of high buildings in dense urban environments and bad reception in indoor environments, the performance of GNSS is significantly degraded. Therefore, alternative ways of positioning and localisation respectively, need to be explored. In indoor environments, unlike outdoors, there is no universal standard, as the different indoor localisation techniques, that are currently implemented have their own bottlenecks. The most widely used Wi-Fi fingerprinting, requires a constantly up-to-date radio map of the signals from the Wi-Fi access points, whose creation is also a heavy and time-consuming technique. Additionally, other techniques require an installation of costly sensors or either equipment.

Therefore, this thesis investigates the possibility of the ceilings in public or semi-public buildings, being used for indoor localisation, by using features that are included in a simple mobile device. The research additionally involves location tracking of different users, in order to discover different movement patterns in an indoor facility. Indoor localisation is achieved based on the comparison of user and reference data, that can be both point clouds and images, using the Light detection and ranging (LiDAR) of an iPad 12 pro and camera sensors of an Android device. The point cloud-based localisation is implemented based on different combinations of global and local registration techniques, while the image-based approach involves different feature detection, description and matching techniques. Using a web-application to visualise the indoor localisation results, an indoor model and a network graph of the Faculty of Architecture and the Built Environment, location tracking of different users is implemented and visualised in a heat-map. Additionally, a dashboard is created that can be used by a facility manager to translate the user paths to valuable information and reveal different movement patterns in an indoor facility.

The followed methodology showed promising results, concerning the reliability of ceilings for real-time indoor localisation, based on LiDAR and camera sensors, that are incorporated in up-to-date mobile devices. The robustness of Colored Iterative Closest Point (ICP) algorithm for indoor localisation based on point clouds was revealed, both in terms of time efficiency and quality, while the combination of Speeded-Up Robust Features (SURF) feature detector and Scale Invariant Feature Transform (SIFT) descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergency situations, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations.

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.

Nowadays, humans rely in technology more and more when it comes to navigation and localisation and in many aspects of life as well. While most concepts related to localisation and navigation of outdoors environments are already well derived from various researches and softwares, the indoor environment remains a significantly unexplored area. Nevertheless, lately there have been increased interest on Location Based Services (LBS) and Indoor Positioning Systems (IPS). There are already several methods available for indoor localisation such as Wi-Fi Fingerprinting and Bluetooth Beacons, but none of them is fully functional yet. It remains a field that requires more and further research and investigation in order to reach a satisfactory and complete Indoor Localisation-Navigation method.
Therefore, this thesis's main objective is to investigate and explore a new method for Indoor Localisation based on Isovists. The exploration and evaluation of Isovist-Fingerprinting approach for Indoor Localisation can extend the fields of LBS and Geomatics. The main research question is “To what extent can isovist support Indoor Localisation” and through this and a series of sub-questions to analyse the Isovist concept in relation to the Indoor Localisation. This is achieved by forming a proof of concept and a methodology that investigates how the Isovists would benefit an LBS.
To succeed that the methodology is divided into 4 main sections. The Data Acquisition for which the newly supported from smartphones Light Detection And Ranging (LiDAR) technology were used. The Space Syntax and Isovist Analysis Measures, where all the concepts related such as the Isovist Parameters were analysed in depth for better understanding of their effect. Then the Matching and Localisation Algorithms, where the possibilities and options on how to reach the localisation were investigated and analysed. And finally, the Tests and Experiments took place in order to evaluate all the prior stages of the methodology.
The main conclusion of this research is that a method for Indoor Localisation based on Isovists is feasible and can indeed support an LBS. The analysis and evaluation of all related components has be done and if putting all the parts in the right order they can be of high value for LBS applications. Since is a new method of Indoor Localisation, there is plenty of future work to be done which mainly focuses on how to connect it with existing techniques and integrate all together into a user application.

Blind and visually impaired people currently have inconveniences locating themselves in the indoor environment. No standardized system exists for them yet. After an inventory of the requirements of blind people, different representations do qualify for providing specific information blind people need. The main research question is: "How can blind people localise themselves (near) real-time in indoor environments with the combination of 3 representations of reality, namely (1) LiDAR point cloud matching, (2) ArcGIS Indoors and (3) Audio dynamic tactile map as the user interface?". Room detection and positioning of the user within the room are obtained by LiDAR scanning and point cloud matching. The processed point cloud height raster grids are acquired and imported into the Esri ArcGIS platform. The rooms are geo-referenced, and data is enriched by contextual awareness. As a user interface for blind people this report proposes two deliverables: a dynamic tactile map and an added or stand-alone audible supported user interface. Preliminary results of the qualitative validation show positive outcomes. This report is a stepping stone for the possibility of integrating multiple into one device.