Earthquakes pose a significant risk to communities worldwide, damaging buildings and disrupting society. This makes it essential to understand how buildings respond to seismic events. Earthquake risk assessments rely on structural building information, typically based on clusters of buildings with similar structural characteristics. Traditionally, this data is collected through time-consuming and expensive field surveys, because many relevant structural attributes are not openly available on a large scale. Meanwhile, developments in 3D city models offer accurate geometric data that could support automated building analysis. However, many models currently lack information on structural characteristics. Therefore, this research aims to explore how 3D city models can be used to automatically extract and predict building typology parameters at the individual building level to improve earthquake risk assessment.

This approach focuses on extracting and predicting Global Earthquake Model (GEM) taxonomy parameters from 3D city building models. First, the geometric and descriptive attributes are extracted using a computational process. Then, correlation analysis is performed to select a unique set of predictive features. Machine learning techniques are then used to predict non-visual structural characteristics, such as the type of walls, foundations, and floors. This study applies the methodology to the area around the Groningen gas field in the Netherlands as a case study. It uses 3DBAG, an automatically generated 3D model of buildings in the Netherlands, alongside a small dataset of structural characteristics of buildings in Groningen obtained through field surveys.

The results demonstrate that geometry-based attributes of the GEM, such as building height, inter-storey height, structural irregularity, roof shape, and floor plan shape, can be reliably derived from 3D building data. Predictions of non-visual structural attributes (i.e., wall, foundation, and floor types, and Lateral Load-Resisting System (LLRS)) reach an accuracy of around 90%, while full vulnerability class predictions achieve approximately 75% accuracy. However, these results should be interpreted with caution due to the small validation dataset and the probabilistic nature of the models. Nevertheless, these findings demonstrate the potential value of 3D city models as data sources for the automated, large-scale retrieval of individual building typology parameters. ... Read more

Despite the maturity of physically based daylight simulation tools, their broad applicability to existing buildings remains constrained. This is partly due to the lack of formal definitions that ensure comparability among models created in different contexts, partly due to inefficient techniques for input acquisition, and partly due to gaps in model calibration.
This work addresses these limitations by first defining different levels of geometric agreement between digital and real indoor spaces, termed Geometrical Levels of Detail (GLoD). These levels represent degrees of geometric completeness and resolution. The study quantifies how those degrees of representation translate into errors in daylight simulation results.
A similar framework is introduced for material inputs through Material Classes of Precision (MCoP). These classes represent different techniques for acquiring optical properties. The propagated uncertainty associated with each level of precision is systematically analysed to determine its influence on daylight simulation results.
Third, a semi-automatic pipeline is developed to reconstruct simulation-ready geometry from LiDAR point clouds. The workflow includes preprocessing, watertight reconstruction of permanent objects, and detection and reconstruction of window boundaries with minimal user interaction. Its performance is evaluated using daylight availability and glare metrics.
Fourth, image-based material characterisation techniques are assessed as accessible alternatives to laboratory measurements. Three techniques are validated, and their influence on daylight simulation results is quantified. A spectral uplifting method is further evaluated to reconstruct full spectral reflectance from RGB inputs for spectral daylight simulations.
Finally, a calibration workflow for indoor spectral daylight simulation is introduced to account for uncertainties related to exterior conditions and window characterisation. Measured spectral irradiance data are used to minimise simulation error. Together, these contributions enable practitioners and researchers to create a robust digital daylight model for existing indoor spaces. ... Read more

Heritage landscapes are experienced, interpreted, and governed through what people see. Visual qualities such as skyline continuity, landmark prominence, enclosure, openness, and view accessibility influence how heritage value is perceived and how spatial interventions are accepted. Meanwhile, urbanization, tourism development, and infrastructure expansion increasingly reshape visual environments, making visual governance a central concern in heritage conservation and landscape planning. Although visual heritage landscape research has grown rapidly, it often remains fragmented: studies tend to privilege either spatial-technical modelling or perception-based evaluation, and the connections between data, methods, and research aims are frequently implicit. This fragmentation limits cross-case comparability, weakens methodological accumulation, and reduces the usability of research outputs for practice. Therefore, the main objective of this thesis is to establish a pathway-oriented framework that links data-method-content configurations, enabling visual evidence to be translated into structured knowledge and practical guidance for spatial decision-making.
A pathway-oriented framework for visual heritage landscape research
This thesis conceptualizes visual heritage landscape research as a set of pathways that integrate three core components: the data used to describe visual environments, the methods used to analyze them, and the content outcomes expected for interpretation and decision support. Instead of treating methods as isolated techniques, the framework emphasizes how different components can be assembled in coherent sequences to match research purposes, spatial scales, and heritage contexts. Based on this logic, four expanded pathway types (EP-1 to EP-4) are proposed to bridge commonly separated approaches and to support more systematic, integrative study designs. Each pathway highlights a distinct integration focus, but collectively they provide a transferable structure for organizing visual research questions, selecting appropriate evidence, and producing outputs that are both analytically rigorous and implementation-oriented.
Pathway implementation through four case studies
The framework is implemented through four case studies that test the expanded pathways across diverse heritage landscape contexts and multi-scale conditions. EP-1 demonstrates an integrated spatial-perceptual pathway that connects spatial structure with perceptual evidence, enabling the interpretation of visual mechanisms and the validation of experienced visual qualities. EP-2 develops a digitally supported perception evaluation pathway that extends visual assessment to larger spatial coverage and multiple viewpoints through digital capture and modelling, providing scalable insight into visual quality and environmental preference. EP-3 proposes a multi-source visual-spatial pathway that integrates heterogeneous geo-data and multi-view analyses to strengthen interpretation across viewpoints and spatial levels, supporting a richer understanding of how visual patterns emerge from landscape structure. EP-4 advances a perception-informed decision pathway that couples perceptual evidence with visibility modelling to generate threshold-style rules and decision-ready outputs for visual impact assessment, planning control, and governance. Across cases, the thesis produces reusable indicators, spatial typologies, and pattern-based knowledge that can inform conservation strategies, design interventions, and management priorities.
Synthesis, navigation, and contributions
Building on cross-case synthesis, the thesis develops a navigational model that supports pathway selection and configuration according to objectives, constraints, data availability, and implementation needs. This model encourages modular entry points, allowing studies to begin from data constraints, methodological strengths, or governance questions, while still remaining comparable within a shared pathway system. Overall, the thesis contributes by structuring a fragmented field into a coherent framework of pathways, offering modular workflows that connect data acquisition-computation-assessment, and translating visual heritage landscape research into evidence-informed, interpretable, and actionable tools. These contributions aim to strengthen the integration of visual evidence into heritage landscape conservation, planning, and design, and to support more transparent and robust decision-making in visually sensitive heritage contexts. ... Read more

Automated analysis and interpretation of 3D urban environments from laser-scanned point clouds has emerged as a critical research area with broad applications in urban planning, land administration, autonomous driving, and navigation. Despite remarkable progress in this field, researchers face two key challenges: (i) the comparatively slower advancement of methodologies for 3D point cloud analysis compared to 2D image-based techniques, and (ii) the difficulty of scaling these methods to large and complex real-world urban environments. This thesis addresses both aspects by exploring methodological innovations in 3D point cloud processing and investigating their applicability to large-scale urban settings, with an overall aim of supporting more robust and reliable interpretation of 3D urban scenes.... ... Read more

This thesis explores and evaluates low-cost, efficient assessment methods for predicting indoor airflow and ventilation to support more accessible, performance-based building design. It addresses two aspects of indoor airflow assessment: (1) automating point-wise measurements, and (2) reducing the computational cost of RANS CFD simulations by leveraging different assumptions and geometry simplifications.

The first part of this thesis explores automating point-wise measurements of air velocity magnitude and temperature to reduce their operational cost. To that end, I designed and validated a line-following robot that stops at marked sampling points and conducts measurements. This line-following robot performs measurements with negligible impact on measurement quality and is four times faster than a human operator.

The second part focuses on identifying the most suitable numerical assumptions that balance the accuracy of the results and the computational cost for modelling breathing jets with background airflow ventilation. I conducted a validation study comparing measurements from the robot and the literature, with results from three computational fluid dynamics solvers that employ three different assumptions about the flow: incompressible isothermal, incompressible thermal, and compressible thermal. Compressible thermal flows model the breathing jet most accurately without increasing computational cost.

Therefore, using the compressible thermal solver, I finally propose a framework for indoor furniture’s geometric level of detail (fLOD) to systematically study the effects of modelling detail on airflow prediction and to reduce the computational cost of mesh generation for CFD simulations. Representing the same furniture at different fLOD can result in differences in airflow velocity up to 100% of the ventilation inlet velocity. Additionally, the ventilation regime, especially the positions of the inlet and outlet, can significantly amplify those differences, at least doubling velocity magnitude differences, tripling temperature differences, and more than quadrupling scalar concentration differences for the same furniture across different fLODs. The choice of fLOD should be guided by the intended application, the variable of interest, and the ventilation regime under consideration.

In conclusion, this thesis has developed low-cost assessment methods for indoor airflow to enable ventilation-based performance-based building design. It has shown that low cost is not necessarily tied to reduced accuracy. This thesis is a first step toward more feasible, iterative design processes that include characterising airflow and ventilation to improve indoor air quality. ... Read more

Accurate alignment of heterogeneous LiDAR point clouds is important for producing high-quality elevation data. This process, known as harmonisation, corrects spatial discrepancies between overlapping datasets collected at different times or using different sensors. A key step in harmonisation is the use of reliable benchmarks, which are features that are clearly detectable in both datasets, to guide the alignment. While artificial targets have been traditionally used as such benchmarks, their deployment is costly and impractical for large-scale or uncoordinated surveys. Consequently, there is growing interest in extracting natural or man-made features directly from LiDAR data to serve as benchmarks.

Road markings have recently been proposed as an alternative benchmark due to their consistent visibility in LiDAR point clouds, particularly through intensity value. As part of the Integrale Hoogtevoorziening Nederland (IHN) initiative, road markings are being explored as benchmarks for national point cloud alignment. However, the performance of the road markings as a co-registration benchmark has not been well researched yet.

This research investigates how the accuracy of automatically extracted road markings affects the quality of point cloud alignment. It builds upon an adaptive extraction method that adjusts intensity thresholds to suit different datasets and applies geometric filtering to generate 3D line representations of road markings. These extracted features are used to align heterogeneous LiDAR point clouds, and their performance is evaluated against manually digitised road markings to assess how extraction quality influences alignment accuracy. To support this evaluation, a RANSAC-weighted centroid alignment approach is proposed, which uses the inlier count of each correspondence as a weight during transformation estimation, aiming to prioritise geometrically stable benchmarks in the alignment process.

The results show that alignment accuracy is influenced by the extraction quality and distribution of road markings. When fewer road markings are available, especially in datasets with larger time gaps due to environmental changes, the spatial distribution has a higher chance of becoming uneven, leading to transformation errors. These errors grow with distance from the benchmark area, increasing alignment inaccuracies across the dataset. ... Read more

The continued growth in the complexity of apartment buildings, and the digitization of building processes have led to a rise in the use of BIM models during the architectural development phase. These models contain a lot of information, but their life cycle often ends once the building is constructed. However, this information can be used by the Dutch Cadastre, to bring legal building registration even closer to 3D reality. BIM based legal registration, researched under the name BIM Legal, is a key driver for this research. Currently in the Netherlands, the separation of ownership is registered through notarial deeds that are managed by the Dutch Cadastre. This is accompanied by division drawings that indicate how apartments are split into private apartment units and shared spaces.
To develop a full BIM Legal registration, it is necessary to also look at buildings without a BIM model. In such cases, BIM Legal models could be derived from existing legal documents, specifically division drawings. This research investigates the (semi-)automatic reconstruction of 3D legal apartment models from 2D vectorized division drawings. The proposed pipeline starts with already vectorized division drawings and applies shape-based georeferencing techniques, estimating storey heights, vertical alignment, and ends with generating 3D BIM Legal models.
The georeferencing methods tested achieved sufficient alignment across the 10 sample buildings. When outside spaces were included in the vectorized division drawing, it resulted in an average containment of 81.5%. When they were removed or non existing it resulted in 95.2% containment. Storey alignment relies on shape similarity and floor-to-floor matching, which performed well in typical cases but struggled with floors with low similarity to the floor below it. The accuracy of height estimation improves when cross sections are included in the division drawing. Otherwise averaged based on values retrieved from the 3DBAG.
The resulting models conform to the BIM Legal standard, written in CityJSON format at a LoD1+. While the schematic nature of division drawings limits the achievable level of detail, and the geometric and positional accuracy, the models offer a valuable 3D visualization of private and shared ownership spaces. Improvements to the prior vectorization would also improve the accuracy and computation time of the 3D reconstruction. Large scale testing is necessary to research the potential incorporation of BIM legal models from division drawings with a complete BIM Legal registration.
This research not only advances the automation of BIM Legal models from division drawings, but also provides methods which can be applied in other 3D reconstructions, such as georeferencing polygons with a reference to a cadastral dataset.
... Read more

The development of society has led to a dramatic change in urban areas. As of 2025, at least 56% of the world's population lives in cities, and it is projected to reach 70% by 2050. Despite occupying only 3% of the Earth's surface, urban areas account for 60% to 80% of global ''energy consumption''. This intensifies the need for accurate and reliable energy demand models to support carbon reduction and energy transition goals.

Urban Building Energy Modelling (UBEM) provides a structured framework for simulating building energy performance at multiple spatial scales. However, UBEM depends heavily on detailed and high-quality data, which is often fragmented or unavailable as open data. Semantic 3D city models (s3DCMs) are one promising data source. These models offer standardised geometric and semantic representations of urban elements in a three-dimensional environment. This thesis investigates the use of s3DCMs and open data to enhance urban energy applications, focusing on the Netherlands as a case study.

The first part of the thesis addresses the models and data requirements of UBEM, with an emphasis on the Dutch official method for calculating energy performance. It evaluates CityGML as a data model for energy-related applications and analyses the availability and suitability of open datasets in the Netherlands for UBEM use.

The second part focuses on the implementation of the corresponding datasets and simulation solutions to compute the energy performance of buildings. It describes the input data sources, their entities, and the relevant attributes, as well as the enrichment of the s3DCM by linking multiple datasets. A CityGML-based testbed for energy-related applications was published as part of this work, representing the municipality of Rijssen-Holten with Buildings, trees and a digital terrain model (DTM). The enriched s3DCM has been used to perform solar analysis.

Subsequently, the thesis outlines the design and implementation of a building energy simulation (BES) solution for computing the net heat demand of buildings. Due to data limitations at country level, the focus remains on net heat demand rather than full primary energy demand. Required parameters for primary energy calculation were unavailable without introducing additional assumptions.

The simulation results cover two case studies: the municipality of Rijssen-Holten and the national building stock of the Netherlands. Outputs are classified by building type and construction period and compared against available Energy Performance Certificate (EPC) data. Although the comparison must be interpreted with caution, it offers a contextual benchmark for the results.

This thesis highlights the value of open data and the procedures required to enhance existing s3DCM for energy-related use. It also proposes additional research directions, including the integration of solar energy simulation results with the created BES to implement hybrid approaches that better reflect the current characteristics of the Dutch building stock. ... Read more

Lightweight and accurate building models have been widely used in diverse applications such as urban planning, virtual reality, and navigation. In recent years, structure-aware building reconstruction has emerged as a crucial research area. Despite significant advancements in measurement techniques such as Light Detection and Ranging (LiDAR) and photogrammetry, the raw data often contains different types of imperfections, such as noise, outliers, and missing regions. These imperfections pose challenges for the accurate and efficient reconstruction of complex building structures. Therefore, this thesis aims to address these challenges by proposing methods for automatic Level of Detail 2 (LoD2) building reconstruction from airborne LiDAR point clouds, and semi-automatic Level of Detail 3 (LoD3) building reconstruction from Multi-View Stereo (MVS) meshes. Throughout the reconstruction process, structural details are easily distorted in the final output due to the inaccuracies and imperfections of input data. Given that regularities such as symmetry are prevalent in building models, they can be leveraged to recover lost or distorted building structures. To facilitate the recovery of symmetry, building elements are projected into facade planes to be two-dimensional (2D) polygonal shapes. Therefore, to obtain accurate and aesthetically pleasing models, this thesis also focuses on recovering the symmetry of these 2D polygonal shapes generated from buildings.

My first contribution is city-scale LoD2 building reconstruction from airborne LiDAR point clouds. While LiDAR data provides rich geometric information, reconstructing detailed building models at such a large scale remains an open problem. This thesis proposes a novel method to tackle this problem, achieving accurate city-scale LoD2 building reconstruction. Firstly, I use footprint data to segment out the point clouds of individual building instances. Then, I detect planar primitives using a region-growing algorithm and infer wall primitives by applying a vertical assumption on the missing regions. Then an abundant set of candidate faces is generated by intersecting the planes derived from roof and wall primitives. Finally, I can obtain a compact building model by selecting the optimal subset of candidate faces through solving an integer programming problem. Geometry constraints are enforced to ensure that the final model is manifold and watertight.

My second contribution is a semi-automatic method for reconstructing LoD3 building models from MVS meshes. While MVS techniques can generate dense and detailed triangular surfaces, creating compact and accurate LoD3 models from them remains challenging due to the limited data resolution. The proposed method is designed to strike a balance between human interactions and automation, aiming to maximize efficiency while minimizing user efforts. The process begins with a coarse segmentation using variational shape approximation. Then, simple and intuitive operations are introduced to refine the segmentation results by solving a multi-label optimization problem. At this stage, the user’s involvement is minimal and limited to providing high-level guidance, ensuring that the system remains user-friendly. Importantly, these interactions are kept to a minimum, allowing users to make adjustments without requiring precise input, making the process more efficient than manual reconstruction. Finally, the face normals and vertices of the mesh are updated based on the refined segmentation, and the layout of the model is regularized to produce an accurate LoD3 building model. This semi-automatic approach combines the strengths of both user input and automated computation, offering a practical solution for detailed building reconstruction that is both effective and user-friendly.

My third contribution is a novel algorithm to automatically symmetrize 2D polygonal shapes, which is essential to regularize the shapes and enhance the visual aesthetics of building models. The method follows a hypothesis-and-selection pipeline. Taking a 2D polygonal shape generated from a building model as input, I first generate a set of potential symmetric edge pairs. Then the initial set is pruned by two simple geometric tests. Finally, a perfectly symmetric shape is obtained by solving a mixed integer quadratic programming problem. Two hard constraints are imposed to ensure that the final shape to be symmetric. The method is also designed to handle partial symmetry in cases where perfect symmetry is not achievable.

In summary, I first automatically reconstruct LoD2 building models from airborne LiDAR point clouds. Then, I reconstruct LoD3 building models from MVS meshes by incorporating user guidance, which depicts a more detailed representation of building models. To obtain more accurate and visually pleasing building models, I propose to symmetrize the 2D polygonal shapes generated from facade elements of reconstructed models. ... Read more

Accurately classifying laser-scanned point cloud data remains a critical challenge in geospatial analysis, particularly due to the complexity and volume of the data. This thesis presents a novel, confidence-aware deep learning framework designed to improve the classification accuracy of point cloud data, specifically focusing on the Actueel Hoogtebestand Nederland (AHN) dataset. The framework integrates geospatial knowledge into the deep learning process, enabling the model not only to refine its predictions through iterative learning but also to enhance the training data along the way via iterative online learning, ensuring continuous improvement in both training data quality and model performance.

The preprocessing phase assigns confidence scores to each point in the point cloud based on local neighborhood properties, with additional input from multispectral imagery (MSI) to further enhance the confidence estimation. These confidence scores are central to the online learning process, where the model prioritizes high-confidence points for training while progressively updating lower-confidence points to improve accuracy. To test the hypothesis that confidence-aware learning can enhance point cloud classification, we selected the KPConv network due to its suitability for handling unstructured data and capturing complex geometric features.

Extensive experiments demonstrate that the proposed framework, particularly with the Online strategy, enables deep learning models to perform better when trained solely on native point cloud attributes (elevation and intensity) compared to models without this strategy. Importantly, the Online strategy qualitatively enhances the training data by refining labels and reducing noise, thereby supporting more robust model performance. While incorporating additional features from aerial imagery showed no overall improvement, specific classes, like High tension and others did see performance gains. ... Read more

The reconstruction of 3D city models has garnered significant interest in recent years. However, the majority of existing reconstruction methods primarily focus on LOD2 models, while LOD3 model reconstruction often relies on manual labor, and the primary data sources are street view images. This research aims to advance this field by reconstructing LOD3 models through the addition of windows and doors to existing LOD2 models, thereby maximizing the utility of available 3D building models, as well as the accurate addition of windows and doors. This research innovatively utilizes aerial oblique images as the data source for extracting building openings and employs 3D BAG LOD2.2 models as the basic 3D building structures. The 3D facades are projected onto the 2D aerial image space using perspective projection and registration is employed on the projection facade and oblique aerial images. Subsequently, Mask R-CNN is employed to detect and extract the building openings from these projections. Following the extraction, the layout of the openings within the same facade is optimized in terms of both size and position. Lastly, the relative positions of the openings on the facade images are combined with the 3D coordinates of the corresponding facade to calculate the positions of the openings in 3D space. This information is then integrated into the LOD3 model, resulting in a more detailed and accurate representation of the buildings.
This approach successfully reconstructs the final LOD3 model in CityJSON format, which passes the val3dity validation. By effectively utilizing existing 3D building models, this approach conserves a considerable amount of computational resources required for reconstruction. The simplicity and high level of automation of this approach make it a promising solution for reconstructing large-scale LOD3 buildings, leading to more accurate and detailed large 3D urban models. ... Read more

In a rapidly evolving digital landscape, 3D city models have become more accurate and complex. Despite their widespread availability of open-source 3D city model datasets, these invaluable resources remain underutilized. Our primary goal centers on the classification of building and roof types. For our client, Spotr, our work directly impacts on their current project of house value estimations for insurance companies. Beyond insurance, it can be used in energy level rating, sustainability assessments, and so on.
In classification schema design, we designed 15 building types and 7 roof types that are compiled with cities in Europe. We labeled 6599 buildings and 2551 roofs in total using CityJSON files of Delft, Rotterdam, Den Haag, Munich, and Berlin. The accuracy for building classification is 70% and for roof classification is 74%.
For future work, we can increase the labeling amount and include more data from different regions. What's more, it can be extended to 2D roof classification: With the help of 3D roof surfaces, we can easily have the bounding box of the roof and additional 3D features can also be used.




... Read more

Modern navigation heavily relies on Global Navigation Satellite Systems (GNSS) and digitized road network databases, but faces limitations in GNSS-denied areas and complex 2D road netowrks. This project addresses these challenges by developing a methodology to create and store a comprehensive 3D road and terrain dataset for enhanced navigation. In collaboration with TomTom, a company that aims to fulfill software requirements, making significant advancements in geolocation technology and societal contributions. The main research question of the project is: ”How can we create a 3D map of roads using information about the center of the road and elevation data?”. The approach to answer this question involves extracting 2D road polygons from centerline data based on width of the roads, the direction and the amount of lanes of them. These 2D polygons undergo enrichment with elevation data, with techniques like filtering, segmentation, and primitive extraction ensuring alignment with the digital terrain model. The methodology encompasses data acquistion, creation of polygons using the centerlines dataset, 2D-to-3D polygon conversion, elevation integration and data storage in CityJSON format. ... Read more

The building permit process in the Netherlands is mostly digitalized, however, there are still some issues. A downsize of information, the manual checking from the municipality, and the duration of the process are some of these issues. To overcome these issues information between 3D building design models, so-called Building Information Modeling (BIM)s, and 3D city models must be exchanged. To exchange interoperable information between BIMs and 3D city models is called integration. In this research, automatic rule checking is performed after the BIM encoded in Industry Foundation Classes (IFC) is converted to a 3D city model encoded in CityJSON. Integration, however, is not as straightforward as it seems. Other researches have been carried out to perform a full integration from 3D city models encoded in CityGML to BIMs encoded in IFC. This is rather complex and so use cases are utilized. In most other researches using the building permit process as a use case, the automatic rule checking is performed in the BIM domain. In this research, a conversion is performed from a BIM encoded in IFC to a 3D city model encoded in CityJSON. The first step in this research is to analyze land use plans and select the most often used rules. These rules are further analyzed on required information to check the rules. For both IFC and CityJSON, the required information for the rules representing the same information as entities in the standards are selected. In the next step, the input models are analyzed on the presence of the entities from the standards. Before the conversion is performed, it is determined which entity will be converted from which input model and whether or not additional information is needed. Finally, the conversion is performed. The 3D city model can be used for rule checking and satisfies the selected rules. The implications of this research are described for the digital building permit process as well as for the integration of the two domains. Guidelines to model correct BIMs for the digital building permit process and further integration are drafted. In conclusion, the tool created in this research works successfully. Automatic rule checking on all the rules in land use plans is technically possible. In practice, automatic rule checking will most likely not take over soon, since rules are still written ambiguously, builders work with 2D drawings mostly, and the Environmental and Planning Act is soon to be established. ... Read more

A digital reconstruction of real-life trees could provide many benefits in fields such as botany, forestry management, biology, and urban planning. Plant growth modelling in particular would enable the analysis of plant structure and behaviour in a customizable, widely applicable and non-destructive manner. Although many data-driven plant reconstruction methods exist, it remains a complex problem due to the intricate branching systems of trees and the need for balancing model soundness with adherence to the often incomplete input data. Modelling plant growth currently proves difficult as well due to the large number of factors involved in the growth process and the level of prior botanical knowledge and/or detailed field data that is often required.

This work uses an automatic MST (Minimum Spanning Tree)-based reconstruction method to obtain tree skeleton models from LiDAR input data. Multiple scans of the same tree, gathered at different years, are related to each other to improve and expand upon the reconstruction. A procedural model is used to simulate the growth in the tree tips using a lobe-based approach and a region-growing algorithm. The growth-based models provide a temporally informed reconstruction that is visually, geometrically and topologically sound. Establishing correspondences in the main structure between timestamps can assist the reconstruction of the tree at a time for which the input data was noisier or incomplete, as well as provide an estimate of the tree's structure in between known data points. This type of reconstruction can be used to both model and study the growth behaviour of trees, for multi-temporal visualisations, and to provide more informed tree models for reconstruction purposes. ... Read more

The flexibility of the Industry Foundation Classes (IFC) format makes it challenging to blindly rely on the data it stores. This is because, to allow its flexibility, the stored data in an IFC file can be missing, incorrect, redundant, or stored in vague ways while the file can still be considered format compliant. This missing reliability is an issue for a format that is so central to the Architecture, Engineering and Construction (AEC) industry. This also severely hampers the chances for automated processes to be applied in the AEC industry. Chances that could reduce the time and cost spend on a construction while enlarging its quality. Prior done research on this subject is sparse. The small subset of this research that covers the ”repair” or reliable extraction of IFC data primarily focuses on idealized situations that do not often occur in practice. Due to the combination of these factors, this master thesis attempts to discover if it is possible to extract reliable data from the unreliable IFC files with an automated method. Because of the large flexibility and broadness of the IFC format, the scope of the thesis had to be limited. This was done by focusing on the extraction/reconstruction of storeys (and their elevations), rooms, and apartments. For these three subjects not only extraction/reconstruction methods have been created, but they have also been implemented in an open source software tool1. To develop reliable processes, the processes have to be based upon reliable data. From the data available in the IFC format, the tangible geometry is considered to be the most reliable. However, not all the other data can be discarded. Only the data that is implicitly stored in the tangible geometry can be ignored without losing information. In practice this means that most, if not all, topologic data and a subset of the semantic data can be ignored. It was found that for the extraction/reconstruction of storeys (and their elevations), a z-value extraction based on created IfcSlab/IfcRoof groups was fairly effective. These groups were made based on their size, height, and location. This process does show a lowered reliability in buildings with complex storey structures. It was found that for the extraction/reconstruction of rooms, a voxelization approach functioned very well. The boolean refinement that followed, which took the shape from a rough voxelization to the precise room shape, showed some reliability issues. Finally, it was found that for the extraction/reconstruction of apartments that rules applied to a created space syntax graph showed promise. The rules that were used were simple, resulting in lower reliability in structures that had more complex room relationships. In conclusion, this research has been unable to give a definite answer to the issues at hand. However, all three the subjects that have been covered showed promising results and can be considered steps towards a solution. ... Read more

In recent years, there has been an exponential growth in the need for 3D spatial information, particularly 3D models representing the geographic information of the urban environment. The existence of a comprehensive integrated 3D model representing the condition of the underground utility networks in accordance with the above-ground city objects is crucial in the ever-increasing infrastructure demands. To acquire such models the related spatial information (geo-information) is required. This information and its quality determine the quality of the models since it describes the functionality, physical properties, semantic details of the urban features as well as the between them relationships (if exist) and interconnections. Currently, there are available various 3D models that, however, are limited in the representation of objects at a city level, with the corresponding underground information about the utility networks supporting cities’ functionality being restricted and/or underdeveloped. Although the modeling of underground utility networks is under development, there are available models that allow for the mapping of the existing information to predefined schema. However, these models have some limitations that restrain the display of the integrated condition with the above-ground city objects as well as they do not support (completely) the interdependencies between networks of different uses.
This thesis focuses on how to develop a three-dimensional model of underground utility networks integrated with above-ground objects, in order to utilize the model in real-world scenarios (disaster management, cost-effective
routes). These networks concern sewage networks (and the stored sub-networks), while the objects are related to the buildings of the study area. For the research, vector datasets were used, related to the existing underground utility networks of a limited area of the Delft University of Technology campus, as well as network elements related to them. For the examination of the proposed model creation, a methodology was developed that could constitute a general approach for relevant applications, considering the condition of the available vector datasets of the similar and/or relevant content.
This methodology is divided into seven stages that concern the preparation of the research and data collection process, the statistical and spatial analysis of their
content, the integration and cleaning approaches to reconstruct invalid information, and the creation of a relational database to store them. Finally, based on the results from these steps model, routing analysis and functionality were implemented to examine the effectiveness of such model in real-world scenarios. According to the results and limitations that arose, suggestions for the harmonization of the data are addressed as well as future application proposals.
Experimentation demonstrates that the developed methodology lead to the creation of a model that, although it represents a simplification of the existing geographical information, it can successfully be used for the implementation of the proposed case studies (disaster management, cost-effective routes). However, taking into account the poor quality of the input data, the model necessitates improvements in order to be used out of the box for other applications, as well as to verify its compliance with available standards supporting the mapping of the including information. ... Read more

By 2050 the municipality of Amsterdam plans to become climate neutral. The historic city center of Amsterdam poses a challenge in this ambition, as it is home to numerous monumental buildings. Conservation is the starting point for the renovation of monumental buildings and consequently additional regulations are in place. Furthermore, each building is unique, thus renovation of monuments requires customization, making it more expensive and unattractive. Research has shown that due to restricted renovation possibilities, the indoor comfort and energy performance of monumental buildings are often below standard.
This research aims to create an energy-neutral renovation for a monumental building in the oldest neighborhood of Amsterdam: the Burgwallen-Oude Zijde. Resulting in the following research question: “How can monumental buildings be renovated to become energy neutral and comfortable for the users, whilst preserving the monumental status?” A renovation design is made for a mixed-use monumental building and elaborated into a generic approach.
Through careful analysis of the Oudezijds Voorburgwal 30, the chosen case study building, it was determined that a significant part of the building is not monumental. Furthermore, it was confirmed that the energy usage of the building is relatively high, and that the indoor comfort is currently not up to standard. To improve the energy performance and indoor comfort, a design is proposed in which different design measures are suggested. The design makes use of the potential of the neighborhood as it uses aquathermal heat (LT) from the canal in combination with heat pumps as basis for the heating of the building. The building is insulated using a combination of exterior and interior insulation, crack & seam filling, and double-glazed windows. For the monumental parts of the building a wall heating system is proposed, in which the walls are slightly heated to minimize energy losses to the exterior (‘WarmBouwen’ concept’). Energy is produced on site using PV roof tiles on the South facing roofs and several other small design interventions are proposed. Evaluation of the proposed design reveals that none of the identified monumental elements are altered, the energy demand of the building is lowered by 73% and that the indoor comfort increases significantly.
By following the proposed generic approach of (1) identifying the heritage values, (2) assessing the current performance of the building, (3) creating concept designs, (4) finalizing the design, and (5) evaluating the design, the remaining energy demand of the proposed design for the Oudezijds Voorburgwal 30 has not been reduced to zero and is thus not energy neutral. This is mostly due to restricted renovation possibilities and limited roof area for on-site energy production. The proposed design does make the Oudezijds Voorburgwal 30 BENG compliant: a step in the right direction, but for monumental buildings to become energy neutral a fairer middle ground between conservation and sustainability should be found. We should ask ourselves: how far should one go to preserve the past if it limits or damages our future? ... Read more

The growing complexity of many projects and applications require methods that help integrate vasts amounts of data coming from different sources. In all cases, semantic interoperability plays an important role - the meaning of content should be organized logically, as to allow machines to interpret it. The Semantic Web vision has been developing standards to support this idea, with the goal of creating a 'web of data', were pieces information can be accessed and linked to one another consistently. The ideas behind Linked Data have been explored in different areas of study. The geospatial domain presents an interesting challenge, as geospatial data offers many different views on the same physical universe. This is especially the case with geo-registries - collections of official geospatial data used by governments. This data already contains links in the form of spatial relations. However, there is no consensus on how these links should take form. Thus, this research will explore how linkable geospatial data from registries actually is by answering the following question:"To what extent can ontology-based solutions using semantic web technologies contribute to the integration and use of data from geo-spatial registries?". This question is broken down in three sub-questions that address ontology-based integration techniques (including tools and languages), the impact of dataset characteristics and the added value of semantic relations. The research includes a literature study to provide a theoretical background, a case study of two Dutch geo-registries (Basisregistratie Grootschalige Topografie and Basisregistrie Topografie) and a conceptual framework that is applied to the case study as a way to explore the main question. The results of this framework reinforce the importance of using instance-level data to understand the connections between different conceptual models and show the unavoidable subjectivity that is involved in the process - from the conceptualization of the data (model) to the creation of query rules. The findings indicate that the capabilities of ontology languages (such as OWL) are not necessarily practical for data from registries, and that custom processes might be necessary depending on the application envisioned by data users. Country-wide registries managed by different data owners and different interpretations of data acquisition rules will lead to inevitable variations in the registration of objects in datasets. And more efforts could be invested in providing quality indicators for alignments. ... Read more

Noise simulations require finding the paths between multiple receiver and source points. In the current approach, only 3D polylines can be used as input to describe the terrain. These 3D polylines are semi-automatically generated, based on the principle of describing the terrain profile with as few height lines as possible. In order to propose a more efficient, standardised and economic modelling approach, a partnership between RIVM/RWS and the 3D Geoinformation Group at TU Delft was launched in 2017, aiming to generate these height lines automatically from the available datasets, namely AHN3, BAG, and BGT, which are publicly available via PDOK for free. However, it was then proposed to prove that the paths between receiver and source points can be directly generated from a TIN without creating the height lines. The following report provides proof of concept to the hypothesis: ‘Using a TIN directly allows automated 3D noise modelling according to the guidelines of CNOSSOS-EU’. A code was written to generate the paths between receiver and source points using an LoD2 TIN. The paths were then checked visually and were fed to test_Cnossos software to prove their validity. Finally, noise maps were generated and compared to noise maps generated with the current method. ... Read more