Indoor navigation in complex public buildings requires more than geometric shortest-path routing. In multi-level station environments, pedestrian movement is shaped by vertical connectors, access-control elements, semantic origin–destination regions, congestion, and heterogeneous walking behaviour. Although Industry Foundation Classes (IFC) Building Information Modeling (BIM) provides rich geometric and semantic information about building elements, it is not directly usable as a navigation or pedestrian simulation model.

This thesis develops and evaluates a reproducible workflow for transforming IFC BIM data into a semantically enriched 2.5D indoor navigation graph for congestion-aware pedestrian routing and simulation. The workflow interprets movement-relevant IFC semantics, extracts floor-based walkable layers, samples valid graph nodes, filters obstacles and restricted areas, and models vertical connectors and access-control elements as semantic graph components. The resulting graph is used as the movement substrate for graph-based routing and agent-based pedestrian simulation.

The workflow is first checked on a simplified two-floor pilot model and subsequently applied to Dadongmen Station of Hefei Metro Line 1. The final default station graph contains 18,454 nodes and 134,852 edges across three public movement levels. Six experiments evaluate static routing, congestion-aware replanning, pedestrian-profile effects, mixed-agent behaviour, algorithm-threshold sensitivity, and demand-load response.

The results show that congestion-aware replanning reduces waiting time and peak queue length, although it may increase mean travel time because agents use longer alternative routes. Elderly agents experience substantially higher travel and waiting burdens than normal agents. A sparse graph variant remains routable but produces larger queues and earlier demand-load degradation. The findings demonstrate that graph connectivity alone is insufficient: indoor navigation graphs should also be evaluated through movement-performance metrics. The results represent controlled methodological validation rather than calibrated operational passenger-flow prediction. ... Read more

3D Gaussian Splatting (3DGS) has emerged as a compelling method for scene reconstruction by producing compact, photorealistic representations of physical environments in real-time. Its application to indoor spaces, however, introduces a fundamental challenge: reconstructing a multi-room building as a single coherent scene is undermined by the frequent occlusions that walls introduce, degrading the sparse reconstruction on which 3DGS training depends. Per-room reconstruction sidesteps this problem but produces a set of independent scenes in arbitrary coordinate frames that have no shared spatial reference.

This paper presents a pipeline that addresses these challenges by integrating IndoorGML's topological model as the organisational backbone for multiple independently reconstructed 3DGS scenes. The pipeline proceeds in four stages. First, per-room 3DGS scenes are reconstructed using Structure from Motion (SfM) and Gaussian training from video-captured image sets. Second, an Umeyama similarity transformation is estimated for each room between its (SfM) coordinate frame and a shared architectural floor plan reference, simultaneously resolving scale ambiguity and establishing a common metric coordinate system across all scenes. Third, the IndoorGML model is supplemented with additional attributes, and ingested into a PostGIS database that preserves the Cell–Node-Edge topological structure of the original schema. Fourth, a web-based viewer queries this database at runtime to enforce geometrically derived navigation constraints within each room, and to trigger topologically-guided transitions between rooms as the user crosses door boundaries.

The pipeline is demonstrated on three adjacent rooms at the Faculty of Architecture and the Built Environment, of the Delft University of Technology. The site comprises two lecture halls and a corridor space. The prototype achieves sustained real-time rendering performance above 60 frames per second on consumer-grade hardware, with navigation constraints successfully preventing the camera from reaching scene regions outside the training range. The results confirm that IndoorGML's topological model, when extended to reference 3DGS scene data and spatial transforms, provides a strong framework for multi-scene indoor navigation. ... Read more

In modern warfare and airborne operations, such as those carried out by drones and helicopters, precise visibility calculations are critical to mission success. However, current automated methods for calculating line-of-sight (LoS) often rely on simplified gridded, meshed, or voxelized 2.5D representations and either ignore vegetation's impact or unrealistically model it as fully opaque. To address these limitations, this research proposes a novel method for calculating fully 3D, non-binary intervisibility directly from airborne LiDAR point clouds. The method leverages Cloud Optimized Point Cloud (COPC) streaming and k-d tree spatial indexing to rapidly query large, remote datasets. Vegetation is automatically segmented using the deep learning Myria3D framework. The algorithm computes viewsheds by modeling terrain, buildings, and semi-transparent vegetation, resulting in a continuous probability score rather than a traditional binary output. This is achieved by adapting the Beer-Lambert law to apply exponential attenuation along a discrete, volumetric LoS. Finally, the results are visualized as 3D spatial volumes or 2D map overlays for practical integration into mission planning workflows, such as safe flight height mapping and visibility-aware routing. This direct point cloud analysis bridges the gap between raw data and high-level spatial decision-making, offering a framework for safer, data-driven mission planning.
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Historical maps are a rich source of information about past urban landscapes, but their contents remain largely inaccessible for computational analysis. This thesis investigates and compares two deep learning approaches for automated urban extent extraction from Dutch historical topographic maps, and applies the results to urban change analysis.

Method 1 applies patch-level image classification using the MapReader framework on the Bonnebladen map series (1:25,000) covering the Province of South Holland. Map sheets are divided into 200-meter square patches and classified into three classes using a fine-tuned ResNet-101 model. A weak classifier trained on 1,768 manually annotated patches achieved a macro-averaged F1-score of 0.900. Inference was applied to 74 sheets across two temporal editions (T1: 1869–1916, T2: 1910–1943), producing georeferenced classified patch datasets at provincial scale. Post-classification comparison between T1 and T2 detected approximately 65 km² of total urban growth in South Holland, with new building blocks as the dominant growth pattern. The results are validated against the HGN-1900 land use dataset, showing broad spatial agreement in urban areas.

Method 2 applies object-level instance segmentation using Mask R-CNN on Rotterdam 1897 municipality maps (1:5,000). To avoid manual annotation, BGT vector data is style-transferred to approximate the historical cartographic style and translated using CycleGAN to generate a synthetic training dataset with automatically aligned annotation masks. The epoch 6 CycleGAN checkpoint is selected based on FID evaluation and visual inspection of building boundary preservation. The Mask R-CNN model trained on this synthetic data achieved a validation mAP50 of 0.8784. After post-processing and shape regularisation, 12,299 building polygon predictions are produced from sheet 3. External evaluation against BGT buildings confirmed to predate 1897 yielded a recall of 41.4%, reflecting both model detection limitations and the substantial post-war urban renewal of Rotterdam following the 1940 bombing.

The two methods differ substantially in their capabilities and requirements. Method 1 scales to provincial level with limited annotation effort and supports statistical-level change analysis, but is sensitive to cartographic style shifts across temporal editions and produces coarse 200-meter raster output. Method 2 produces building-level vector polygons enabling urban persistence analysis, but requires a complex multi-stage pipeline, is constrained by georeferencing quality, and depends on synthetic training data that underrepresents dense historic urban configurations. Both methods are fully operational on a consumer-grade GPU laptop, confirming their accessibility without HPC infrastructure. Together, they demonstrate that IIIF-based map access infrastructure and modern deep learning techniques can be combined to support reproducible urban change analysis from historical map collections at multiple scales of spatial granularity. ... Read more

3D Gaussian Splatting (3DGS) represents scenes as collections of Gaussian primitives whose attributes are shaped by multi-view photographic supervision. This raises a natural question: does the photographer’s visual focus leave a measurable imprint on these intrinsic properties? While prior work has explored segmentation and scene decomposition in 3DGS, no existing method has investigated whether Gaussian attributes alone encode visual saliency. We propose a mask-free, post hoc classifier that recovers the photographer’s region of interest from Gaussian attributes, requiring neither the original training images nor any 2D foundation model. Trained on scenes from Tanks and Temples and MipNeRF360, our method achieves a mean LOOCV F1 of 0.957 and generalizes to unseen scenes with a mean test F1 of 0.929. Projected 3D saliency masks show strong alignment with U2-Net predictions on original training images, confirming that multi-view Gaussian intrinsic properties capture a geometrically consistent, view-stable notion of saliency that single-frame 2D methods cannot provide. These properties make our method applicable to automatic foreground extraction, capture intent analysis, and perceptual quality-driven compression for bandwidth-efficient streaming. ... Read more

Heritage point clouds are an increasingly common tool used in the conservation and documentation of cultural assets. Yet, their geometric complexity, uniqueness, and scale make automated processing using machine learning or deep learning difficult. Mathematical morphology offers an alternative using geometric assumptions in the form of a structuring element, requiring no training data and remaining interpretable. This thesis extends the point-based mathematical morphology algorithms proposed by Balado et al. (2020) to make them useful for real-world heritage point cloud processing. The original algorithms are optimized using a parallel implementation on the GPU in the form of compute shaders, reducing the time complexity of both erosion and dilation to O(n) and achieving a speedup of approximately 100x over the sequential baseline. Four extensions to the algorithms are created: a continuous erosion score replacing the binary output, orientation-aware operations using TNB matrix rotation with both a predetermined and a brute-force variant, density-aware structuring elements in discrete and continuous forms, and a set of helper functions enabling compound morphological operations. These contributions are evaluated on three heritage applications. Segmentation using sequential morphological opening and hit-or-miss transform achieves a mIoU of 0.623 and an overall accuracy of 0.813 on the Images&PointClouds Cultural Heritage Dataset without parameter optimization. Object detection achieves F1 scores above 0.920 on the Fontana dei Mesi dataset. Gap filling via morphological closing performs well locally but is sensitive to the geometric complexity of the scene when applied globally, with edge detection and template matching providing more consistent results. The results show that point-based mathematical morphology, when extended with the contributions of this thesis, is a practical and effective tool for heritage point cloud processing that does not require annotated training data. ... Read more

High-resolution aerial imagery plays a critical role in urban planning, energy mapping, and land-use classification. However, many datasets remain limited to lower resolutions due to acquisition costs or legacy data sources. Super-resolution (SR) techniques offer a means to enhance 25 cm aerial imagery to 8 cm, making it more suitable for object-level analysis. This thesis investigates the capability of a modified SRGAN architecture to enhance the visual and structural fidelity of aerial images, thereby improving the representation of urban features such as rooftops, dormers, and solar panels. The architecture incorporates an EdgeMaskBlock to improve edge awareness and preserve sharp contours in reconstructed imagery. To address the challenges of spatial complexity and temporal misalignment, a two-phase training strategy is implemented. First, the model is trained on synthetically downsampled HR-LR pairs to establish a robust initialization. This is followed by fine-tuning on real-world 25 cm inputs mis- aligned with their 8 cm HR counterparts, enabling the model to generalize under realistic and variable acquisition conditions. Evaluation is conducted across both training iterations using standard image quality metrics (PSNR, SSIM, LPIPS), along with downstream segmentation benchmarks. For Iteration 2, the gen- eralization capability of the model is assessed across new cities and seasonal conditions. Two segmen- tation pipelines are used: the Segment Anything Model (SAM) and the operational semantic segmen- tation system developed by Readar B.V., which detects buildings, dormers, and PV panels using both RGB and DSM data. Metrics such as precision, recall, and F1-score demonstrate that super-resolved outputs significantly outperform bicubic upsampling, particularly for fine-scale rooftop objects. The results show that the proposed SRGAN model improves perceptual quality while enabling effective domain transfer across seasons. These enhancements contribute to more reliable segmentation outputs, reinforcing the potential of GAN-based super-resolution as a practical tool in geospatial workflows that require fine-grained object recognition. ... Read more

Map labels play an important role in helping users interpret geographic information. Traditional label placement algorithms are typically designed for static or fixed-scale maps, where labels appear at discrete, predefined zoom levels. However, vario-scale maps change content continuously during zooming, requiring labels to also transition smoothly to maintain readability and visual coherence.
This research explores how to place and adjust labels on vario-scale maps, ensuring legibility and usability throughout scale transitions. Due to limited existing work in this area, a set of requirements, both hard and soft, are defined to guide the methodology and evaluate results.
Different strategies are applied based on geometry types. For elongated features such as roads and rivers, label anchor points and orientations are derived from the medial axis (skeleton). For compact geometries such as buildings, the centroid and pole of inaccessibility are used to determine anchor points, while the minimum rotated bounding rectangle guides label rotation.
Two polygonization methods are developed using the tGAP structure: slice-based method extracts geometry at fixed scales and computes label positions per feature type; event-based method reacts to changes in geometry, computing label anchors whenever a change occurs. Label anchor points could be represented as continuous trajectories, supporting smooth interpolation. During interactive visualization, label positions, orientations, and opacities are interpolated across scales. To prevent clutter, real-time collision detection is implemented.
The methodology was applied to the TOP10NL dataset in the Delft region. Results showed that the event-based method allows more precise responsiveness to geometry changes but can produce occasional abrupt label movements. In contrast, the slice-based method offers more stable transitions but may cause unnecessary label shifts even when geometries do not change. Event-based labeling also resulted in a higher number of anchor points. While hard requirements were generally satisfied, some soft requirements were not fully met due to intentional trade-offs or implementation limitations.
Overall, this research introduces a novel framework for integrating dynamic labeling into vario-scale maps using the tGAP structure and demonstrates its feasibility through an interactive, real-time prototype with smooth label transitions and collision handling. ... Read more

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. ... Read more

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 ... Read more

Urban freight transport is a critical yet complex component of city logistics, shaped not only by transport networks but also by the morphological structure of urban areas. Traditional forecasting models often neglect this spatial heterogeneity, relying primarily on traffic counts or infrastructure topology. This thesis proposes and evaluates ST-SimNet, a Spatio-Temporal Simulation Network designed to enhance freight flow prediction by integrating static urban morphology descriptors with dynamic freight data in a graph neural network framework. Focusing on the city of Amsterdam, the study explores the extent to which detailed urban morphology, including building features, land use, and spatial layout, can improve short-term freight flow forecasts at the road network level. Results demonstrate that incorporating static features significantly reduces error variance, improves peak hour prediction, and enhances node-level stability compared to dynamic-only baselines. Furthermore, analysis reveals that nodes with richer morphology information benefit most, while areas with sparse or noisy static features experience challenges that highlight opportunities for future refinement. The findings offer practical insights for integrating machine learning into digital twin platforms for urban mobility, providing a data-driven, spatially aware layer for freight forecasting in operational city planning systems. Limitations and future directions, including adaptive fusion mechanisms and cross-city generalisation, are discussed. Overall, ST-SimNet advances the integration of urban morphology into spatio-temporal predictive models and demonstrates its practical relevance for modern freight planning in complex urban environments. ... Read more

To help prevent flooding of rivers and cities, Dutch maritime contractor Van Oord regularly dredged 52 side channels as part of the Dutch Department of Waterways and Public Works' (Rijkswaterstaat) "Room for Rivers" strategy. Side channels make rivers more resilient to flooding by providing increased flow capacity, buffer space, and a secondary path downstream for water. Van Oord wishes to know how they can better leverage their growing historical data collection to enable predictive maintenance of side channels in the form of dredging.
Instead of developing a complex hydrological model, which would require deep knowledge of river morphology. We, as Geomatics students, extracted insights directly from the available geospatial data. For our 10-week MSc Geomatics Synthesis Project, our main research question is as follows: "How can the features of a side channel be identified and extracted to enable predictive maintenance?"
In order to answer this question for our client Van Oord, we performed a literature review and interviewed domain experts to identify relevant characteristics of side channels. Then, we explored the available geo-spatial data to determine which characteristics can be modeled as features, before processing the data in an FME pipeline to calculate these feature values in an automated, extendible, and understandable way. These features were then stored in a geo-spatial database. Reading from this database, we created a prototype machine learning model that takes the features as input. The model enables analysis of the side channels to derive insights into the sedimentation of side channels, reaching 84% accuracy within a 5cm error for the Bakenhof channel.

The result is a robust FME-based data processing pipeline, a geo-spatial database with 19 unique features for 26 suitable side channels, and a prototype neural network showing significant predictive ability. The product enables the client to better estimate side channel behavior, enabling informed predictive maintenance, as well as allowing the client to better decide moments when expensive channel measurements can be skipped. ... Read more

This thesis explores methods to enhance the usability of vario-scale maps, focusing on user interaction, cognitive load, and overall satisfaction. Vario-scale maps, based on a continuous zooming approach introduced by Van Oosterom and Meijers, aim to reduce visual disruptions by allowing smooth generalisation of geographic data in real time. The research highlights their potential to merge precision and aesthetic clarity wxhile advancing beyond the conventional snapping behaviour inherent in multi-scale interfaces.
A mixed-method approach was adopted to investigate whether vario-scale maps could improve navigation and user satisfaction. Data were collected through a pilot web-based usability study, followed by a laboratory eye-tracking study designed to capture detailed behavioural and cognitive processes. Quantitative metrics, such as fixation counts and scanpath lengths, were analysed to determine efficiency in map-use tasks, while think-aloud protocols and questionnaires provided qualitative insights into participant preferences and frustrations.
Findings indicate that vario-scale and multi-scale maps offer similar levels of efficiency based on objective eye-tracking metrics. However, participants generally reported higher satisfaction with vario-scale maps, citing smoother transitions, less visual interruption, and a more intuitive zooming experience. The pilot study underscored the importance of user familiarity with Cartography, the careful placement of icons, and task complexity as influential factors in map usability. These insights informed the final study, which showed that vario-scale maps align more closely with user expectations for modern digital interfaces.
By emphasising controlled task design, participant comfort, and iterative refinement through pilot testing, this thesis demonstrates how vario-scale maps can be optimised for enhanced usability. The results further suggest that leveraging cartographic principles—such as appropriate symbol density-plays an essential role in maintaining clarity and facilitating user orientation. Through comprehensive analysis of both objective metrics and subjective evaluations, this thesis provides evidence that improving vario-scale usability is both feasible and beneficial. Future research with broader user demographics and extended usage contexts is recommended to validate and refine the proposed design strategies, ultimately supporting a wider adoption of vario-scale technology for interactive and dynamic mapping applications. ... Read more

Geo-information has many applications in various sectors and is used by public agencies as well as the private sector. A key method for sharing and visualising geo-information is through web viewers. Extensive infrastructure exists for creating web viewers to publish 2D geo-information without the need of coding, much of which is based on OpenLayers.

3D geo-information is becoming increasingly important for many use cases, such as urban planning, asset management, and disaster management. Consequently, there is an increasing need to publish 3D geo-information in web viewers without the need of coding. Since applications to do this for 2D geo-information are widely in use, it would be useful to be able to effectively integrate 3D functionality into an existing application.

This research investigates the needs for integrating 3D functionality into an existing OpenLayers based GIS web application that allows the user to publish geo-information in web viewers. This includes both the technical needs, e.i. how to integrate the necessary code, and the user needs, e.i. what features does the user need to make the application useful and user-friendly.

A proof of concept was developed to investigate the technical needs. The proof of concept is an extension of the existing application Tailormap. Tailormap is based on OpenLayers and allows users to publish geo-information in web viewers without the need of coding. 3D functionality is integrated into Tailormap to allow the users to publish 3D vector data, such as buildings, alongside 2D OpenLayers maps. This integration was achieved by incorporating Cesium functionality to load and visualise 3D Tiles datasets.

To ascertain the user needs, interviews were conducted with Tailormap users and the created proof of concept was tested with them to gather feedback. The interviews and testing sessions revealed that the most critical type of 3D data for publishing in a web viewer was vector data, specifically buildings. Furthermore, specific functions were identified that make the application useful for the users.

The developed technical methodology and the identified user needs are primarily applicable to integrating 3D functionality into Tailormap but can also serve as guidelines for integrating 3D functionality into other OpenLayers-based GIS web applications for publishing geo-information in web viewers. ... Read more

With the proliferation of the IoT infrastructure, the trajectory data is dynamically emerging. This data originates from a variety of moving objects, containing big volumes of multi-dimensional information such as space, time, semantics etc. The underlying information can be potentially applied to create added value through scientific research, decision-making, emergency management etc.

However, due to the special properties of the trajectory data, namely high frequency, cardinality, dimensionality, heterogeneity etc., traditional data management systems face difficulties in handling such data. Even though some distributed solutions or big data solutions exist in other fields, they are not designed considering the modelling, accessing, distributing and querying characteristics of this special spatio-temporal data.

Given the spatial data management problems, a clustering/indexing solution for high dimensional Point Cloud by Space-filling curve considering the heterogeneous data spatial distribution has been developed, advocated and validated by a series of research finished at the GDMC, TU Delft.

However, it is uncertain whether the framework can be extended to other types of space-related phenomena. Furthermore, whether distributed database techniques can be utilized remains to be explored and what adjustments should be made is still unclear. To some extent, this thesis is an expanded study based on the Point Cloud research mentioned above.

To address these data management challenges, this thesis focuses on trajectory data modelling and compression, indexing and clustering, partitioning and distributing. Also, the querying strategies are studied. More specifically, the three main results of this thesis are:

Model the trajectory as the sequence split by semantic attributes and a spatio-temporal cube. This modelling takes proximity (locality) preservation and trajectory preservation into account at the same time, resulting in a balanced level of flexibility and aggregation, mitigating the storage burden by row-wise compression. For different subdivision resolutions (depth of Octree for space partitioning), the compression ratio can be up to 10.

Access the trajectory data by Space-filling Curve. The Space-filling Curve indexing method maps the 3D (high dimensional) indices to 1D (low dimensional) indices, overcoming the contradiction between high dimensionality and high cardinality. Adaptive Octree is used to mitigate the heterogeneity of the trajectory data. Based on the experiment results, the optimal tree depth is 4 or 5. The query optimization (specifically, the range merging technique) is also preliminarily explored.

Distribute the data on the distributed machines. This distributed deployment results in higher (nearly linearly in the experiment) scalability (horizontal expansion of disk, memory and CPU resources) and speed-up. The Space-filling Curve distributing strategy results in better load-balancing. However, due to the lack of flexibility of the distributed database platform used (specifically, Greenplum), the localization of data and computation (such as local aggregation) is limited. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

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. ... Read more

This research examines the multifaceted challenges posed by urbanization in the Netherlands, specifically focusing on the intricate relationships between modes of mobility and societal well-being. Central to this exploration is studying the modal split and understanding how shifts in this split can have profound implications for urban areas. The Dutch Ministry of Infrastructure and Water Management’s (Min IenW) Well-being Monitor is the foundation for this study, aiming to chart the interplay of living environment, safety, health, and accessibility. It illuminates the potential consequences of changing transportation choices (modal split) on broader environmental concerns such as the Urban Heat Island effect, noise disturbance, air quality, and climate resilience. While cars due to their ubiquity, cars play a central role in the research. However, the importance of Non-Motorized Transport (NMT) and Public Transport (PT) is also highlighted, given their significant roles in urban mobility and their potential to redefine the modal split. Using Rotterdam as a case study, the research delves into the spatial constraints of one of the Netherlands’ largest cities, emphasizing the need for effective urban planning that prioritizes sustainable transportation solutions and envisions future shifts in transportation preferences. The goal is to offer valuable insights that can influence urban strategies, ensuring sustainable and livable cities. ... Read more