The rise of urban digital twins emphasises the critical role of representing real-world environments for decision-making and collaboration among stakeholders. This leads to an expectation of effective management and visualisation of large-scale urban data models. 3D Tiles is an open specification designed for streaming and rendering massive heterogeneous 3D geospatial datasets. It is widely adopted in fields such as urban planning and engineering construction, where geospatial data is crucial for decision-making and collaboration.

However, as the 3D data size grows, the performance of traditional file-based solutions is facing challenges. Currently, dissemination and visualisation of 3D data are often reliant on file-based systems. Users suffer from problems such as long load times, data inconsistencies, and lack of flexibility. In this thesis, an approach for compactly storing both geometries and attributes in the Database Management System (DBMS) and efficiently serving data compliant with 3D Web standards to the client is proposed, advocating the direct serving of 3D Tiles from the database.

Point cloud data are gaining more importance in the Geomatics domain, with the development of modern sensing technologies like LiDAR and photogrammetry. The size of massive point clouds are growing, and the performance of traditional database solutions for its data management become insufficient. It remains a huge challenge for researchers to come up with a data management solution that handles the huge volumes of data, while providing standardized functionalities.

Space Filling Curve (SFC) has been explored as a good spaital access techniques for organizing point clouds. Existing SFC-based database solutions include PlainSFC, HistSFC, etc. However, they use a flat table to store point records and it is not compact for massive point clouds. In this thesis, a SFC-based database solution that manages point cloud in blocks is proposed. The purpose is to improve the performance of current point cloud database solutions, especially with storage space. This model organizes the point clouds based on Space Filling Curve, and innovatively splits each SFC key to a head and a tile. The points with the same SFC head are placed in the same block. SFC tails and other property dimensions are stored as arrays in other columns.

Compared with the pgPointCloud and Oracle SDO_PC, the intermediate SplitSFC prototype does not show significant advantage in storage and data retrieval efficiency so far. However, it is fair to believe that with the improvement of algorithms and implementations, it has the potential to be an approximate and efficient point cloud data management solution.

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.

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.

Subsidence is affecting different parts of the Netherlands. The strongest subsidence is observed in the province of Groningen due to ongoing natural gas extraction. Subsidence is also observed at several locations in the province of South Holland, where the processes of peat oxidation, soil compaction and the withdrawal of groundwater are at the root of the problem. In South Limburg, the after-effects of coal mining are seen in the surface deformation, which is characterised by ground heave due to rising mine water, and the potential risks for sinkhole and local subsidence due to near-surface mining. Not only does subsidence cause damage to the natural and built environment, but it also increases the vulnerability towards flooding. Considering the rise in sea level, subsidence forms a pressing issue for low-lying countries, such as the Netherlands. The combined efforts of interferometric synthetic-aperture radar (InSAR) data, global positioning system (GPS) points and gravity measurements have led to the recent Bodemdalingskaart (2018), which represents the nationwide subsidence in three statistically inferred products. These products are the the total surface deformation, the deformation caused by shallow subsurface processes, and the deformation caused by deep subsurface processes. This has triggered the discussion on how the deformation from 'shallow' and 'deep' processes can or should be better understood in a physical sense. The aim of this research is to 'enrich' Sentinel-1 persistent scatterer interferometry (PSI) points with contextual information, or 'attribute-enrichment', in order to better understand the origin of the observed deformation. Classifications from the Dutch 'basisregistraties' (base registries) are assigned to the PSI points, which are stored in a spatial database. The classifications include information on the Dutch soil types and geomorphology. The classifications from Bodemkaart are used in an analysis of nationwide extent, focused on the deformation behaviour of different soils and their groundwater levels. The nationwide average deformation of all track results suggest subsiding trends of about -1.24 mm/yr for marine clay soils and -0.48 mm/yr for peat soils, whereas opposite trends of uplift are observed of, on average, +0.77 mm/yr for river clay soils and +0.54 mm/yr for sand. The soil and geomorphology datasets are also used in combination with the geographic classifications of the built environment to study the distinction between deep and shallow-induced deformation in Groningen and South Holland. Here, the 2 x 2 km grid cell representation of deformation makes it possible to characterise areas with overall low PSI point density. In the localised deformation cases, the behaviour of classes from different attributes can be directly compared, e.g. road polygons intersecting with soil type polygons. For South Limburg, risk-assessment-based classifications are used to enrich the PSI points in the study the coal mining after-effects in the identified risk areas. The risk-deformation results suggest an overall slowing trend in the ground heave of the potential impact areas. The results from the case studies highlight the efficiency of attribute-enriched PSI datasets for the interpretation of deformation, based on both direct and indirect physical classifications.

Spatial data acquisition is rapidly developing, making point clouds available and easily accessible for many applications and to many end-users. Nowadays, point clouds are the main surveying product and have more added value than derived products as they keep details and they are not interpolated. Raw point clouds do not contain information that relates the points to the semantic meanings of the real word objects that are represented. Moreover, through different procedures (classification and segmentation techniques) important semantic information could be derived, creating an intelligent environment and structure, a Smart Point Cloud (SPC). In this research, a SPC framework will be created combining different techniques and methods in order to detect the pipes in an industrial environment. Close-range photogrammetry will be used to generate a point cloud, for which panoramic images are the main source data. The features and the attributes from both the data (panoramic images and point cloud) will be combined to get characteristics from both sources (2D & 3D) in order to select, analyze, manipulate and identify the pipes as one object.

The publicly funded ESA Copernicus and NASA Landsat earth observation programs are publishing their data as open data in the promise of 30 billion euro of financial benefit for the EU as well as 50.000 new jobs, both by 2023. The move by these two space agencies fits in a larger pattern of ‘new space’, in which the launch and exploitation of satellites are moved from national agencies to private commercial stakeholders. The data offered by ESA and NASA are published as open data intended for reuse with the objective to create viable value-adding earth observation services, i.e. services which use big and open earth observation as a resource for analytics and provide incentives for all participants, including the client and user, to continue the service provision. Furthermore, the earth observation data are big data, as it satisfies the criteria of high volume, variety and velocity increasing the effort required for handling the data. Even though the market for value-adding earth observation services is growing and has the potential to become the most important market in the whole earth observation value chain from rocket to service, the initial promises of the release of the data are not going to be accomplished at the current pace.

Within searched academic literature and during conversations with practitioners, the lack of a structured approach to creating these viable value-adding services has been observed. Furthermore, the research articles on this topic are very much technology-oriented, with only very few articles invoking the interests of stakeholders to make a service design viable. And none of these followed a method for creating the service. This thesis addresses the lack of such a method for the creation of viable services using big and open earth observation data as a resource. Specifically, four knowledge gaps are identified: The first and second knowledge gaps concerned the lack of viable services which used a structured method for their design in the academic literature of respectively the narrow focus area of big and open earth observation data as well as in the larger area of earth observations which includes the previous narrow focus area. The third knowledge gap concerns the lack of factors of big and open earth observation data which influence the viability of a service design within the searched academic literature and the fourth concerns the actual lack of a structured method to guide the design of viable services which use big and open earth observation data as a resource. Together, these knowledge gaps lead to the formulation of the following research objective: “to design a method targeted at service providers for the creation of viable services which use big and open earth observation data as a resource.” The artefact which is developed is a method which guides a service designer through the process of creating a viable service. Following the method results in creating a service design which is both feasible, i.e. technologically possible, and viable, i.e. provides sufficient incentives for all stakeholders involved to carry on its provision. The approach for structuring the creation of the method is the design science research approach for information systems. The demarcation of the problem is the first activity within this approach, and it uses a structured literature review as well unstructured explorative interviews. This is followed by a literature review on influencing factors, using only a structured literature review as a research method. The requirements gathering activity employs a case study method, in which participant-observations and interviews are used for information gathering within three cases. Then, creative methods are used in the design activity to create the artefact. For the following demonstration of the artefact, a case study is again used. Finally, for the evaluation of the artefact a survey based on the combined UTAUT-ECT theory and observations are used to evaluate whether the artefact attains its objectives, and improvements are suggested based on the evaluation and the demonstration. During this research, the author was a research intern at CGI Netherlands in the Space, Defence and Intelligence department, which allowed for rich observations and access to cases.

The result is the SIMEO-STOF method consisting of five phases. It combines the Service Innovation Method for Earth Observation (SIMEO) with the STOF model tailored for the earth observation domain. The first phase guides a designer through the process of finding together with a client a new service idea based on the current capabilities of earth observation analytics and the business processes of the client. Linking the earth observation capabilities to the limitations of current earth observation data supply allows for the rapid exclusion of unfeasible or unviable ideas. Any ideas that pass this initial set of limitations can proceed to the second phase. In this service domain, essential aspects of the value proposition are defined. This is followed by the technology phase, where principles of security and big data computing are set as well as a first architecture of the IT systems. Then, acquisition of external resources and inter-organizational relations are discussed in the organizational phase. Finally, the amount and form of value retained by the service provider are discussed in the financial phase. As a demonstration, the artefact is applied to the case of a crude and refined oil transhipment provider in the harbour of Rotterdam. The result of the application is a service design which is most likely to be viable, considering some issues still need resolving. Whilst not being definitively viable, the identification of issues which require resolution to achieve viability is an outcome which gets as close as possible to a viable design.

The artefact is evaluated for internal and external validity, respectively whether it fulfils the quality requirements and whether it fulfils its objective. Both were included in a survey held amongst employees of a service providing company interested in the use of the SIMEO-STOF method. Whilst the limited number of responses did not allow for any statistical analysis, the descriptive statistics and observations allowed for the concussion of a generally positive evaluation. Measured by the attitude towards using the SIMEO-STOF method, the method fulfils its objective of facilitating the creation of viable services which use big and open earth observation data as a resource. Whilst there is a mixed response to the quality requirements, observations indicate that part of these can be explained by different levels of expectation. Some participants thought of a future use for the artefact and judged the quality requirement based on this instead of the actual objective.

In terms of academic contributions, this research contributes to all four identified knowledge gaps. Foremost, it provides a viable service design method in form of the SIMEO-STOF method. This extends the service design literature to the application domain of earth observations, which has not previously been viewed from this perspective. Furthermore, factors of big and open earth observation data that contribute to the understanding of its effects on viable service design have been identified, in direct response to a further knowledge gap. Ultimately, the result of the SIMEO-STOF method demonstration is a viable service design created with a structured method, which is a contribution to the first and second knowledge gaps. In terms of societal contributions, the SIEMO-STOF method may accelerate growth in the ‘value-added EO services’ market, not only allowing service providers to more efficiently and effectively design viable service and create value, it may also lead to increased market growth on the suppliers side and an argument for open data publishers for the value of their activities. Though the use of the SIMEO-STOF method, EO service providers may be able to achieve the expected breakthrough of EO services within the broader society, which is one of the objectives of the ESA open data portal.

One of the principal limitations of this research comes from the choice of case study as the main research method, which limits the generality of the results. This is valid for the environment form which requirements are gathered, the type of data used and the design perspective of a service provider which is taken. The author suggests further research to include cases which cover aspects previously not included, for example, a business to consumer service, non-satellite earth observation data, and the inclusion of cases from different service providers. Another important limitation concerns the novel application of the UTAUT-ECT theory in a novel wats, including to an information system which is not a practical implementation of a technology but a method. Combined with the reduced number of respondents in the survey, the UTAUT-ECT-model adapted from theory for this research could not be tested statistically. Future research could focus on repeating the novel application of the theory with sufficient respondents to allow for validation of its application.

The objective of this research is the design of a method targeted at service providers for the creation of viable services which use big and open earth observation data as a resource. Considering the successful demonstration, and the generally positive evaluation of the SIMEO-STOF method, the author considers this objective to be attained.

This research focuses on a smooth rendering of continuous 2D map based on a smooth 3D vario-scale geographical data structure. A Space Scale Cube (SSC) offers non-redundant geometric data for the different level of details. SSC model represents geographic data as a closed polyhedron, to generate a 2D map; SSC is intersected with the projection plane; resulting in a set of 2D polygons. However, problems emerge when creating maps with a large sized SSC dataset under web environment due to limited bandwidth and decoding speed. Repetitively transmitting data from the server to the client can be time and bandwidth consuming. A preprocess should be applied to a source that allows the follow-up development of an online traffic and time-efficient prototype.
After preprocessing, large sized data will be subdivided based on octree algorithm to minimize transmission time from server to the client; moreover, accessible to WebGL. A prototype has been developed which enables smooth and timely vario-scale map rendering against heavy user actions such as fast zooming and panning in a short period. Modified prototype program allows query of only relevant data chunks by current viewport position; it prevents repeated loading of same chunks; what is more, repeated transmission of data from outside to GPU is eliminated. A tree structure is embedded at the client side that facilitates retrieve time. Rendering happens every frame; hence the prototype responses to heavy user actions timely. Also, it can obtain coordinates in RD coordinate system by double clicking. After testing the modified program with a 9km by 9km dataset online, exceptional performance is indicated by a high average frame rate (57 fps) and low main memory occupation (with a network speed at 9MB/s). The prototype performance is significant affected by the client network condition; low network speed can decrease the frame rate. For instance, the web service achieved a frame rate of 47 fps at a network speed at 6MB/s.