This thesis addresses the challenge of extracting wireframe models which consist of 3d line segments from point clouds of man-made urban linear objects, with a specific focus on power lines and pylons. Wireframe models are essential for various applications including 3D city modeling, infrastructure monitoring, and urban planning. However, the automatic extraction of accurate wireframes from sparse airborne LiDAR point clouds remains challenging due to the complexity of these structures. Current wireframe extraction research either require high-quality data for model fitting or depend on complex pre-processing steps, lacking generality. To address these challenges, this thesis proposes a comprehensive evaluation of multiple wireframe extraction approaches and introduces an energy minimization framework which aims to address the limitations of the existing algorithms.

This thesis investigates four different algorithms for wireframe extraction: 3D RANSAC, 3D-2D RANSAC, Region Growing, and Hough Transform to address their limitations. Additionally, an approach of energy minimization for Markov Random Field is proposed to explore the potential of energy minimization methods in wireframe model extraction. Each algorithm is evaluated using a dataset of power lines and pylons from the Netherlands, with manually extracted wireframes serving as ground truth.

Experimental results demonstrate that each algorithm exhibits distinct advantages and limitations. The 3D RANSAC algorithm struggles with cylinder radius estimation and overlooks significant portions of input data. The 3D-2D RANSAC approach reduces dependency on normal estimation but still faces challenges with fitting accuracy. Region Growing achieves lower overlooking rates but suffers from scattered distribution of extracted elements. Hough Transform performs well on simple structures without requiring normal information but becomes computationally expensive for complex cases. The proposed energy minimization method shows promising results in preserving structural integrity by processing dense input graphs, particularly for complex structures with internal components.

Common limitations across all approaches include difficulties in normal estimation from sparse point clouds, misalignment between extracted primitives and ground truth, and challenges in balancing completeness and accuracy. The research emphasizes the complexity of wireframe extraction from point clouds and provides insights for developing more robust methods that combine the strengths of different approaches while addressing their mutual limitations.

Urban digital twins rely on accurate rooftop geometry, yet airborne lidar point clouds are frequently sparse and incomplete, leading to substantial information loss in building reconstruction. This thesis investigates diffusion--based learning as a remedy for high-fidelity roof recovery under severe data corruption.

This thesis proposes a two-stage framework that operates on 2.5D height-map representations. Stage~I introduces a dual-task diffusion model that jointly performs roof height-map completion and roof-line prediction. A novel Bidirectional Control Module enables reciprocal conditioning between the two tasks, enforcing geometric consistency during the denoising process. Stage~II employs a patch-based diffusion upsampler equipped with positional embeddings and a domain-specific global context encoder to synthesise high-resolution height maps while remaining computationally tractable for large and variably-sized buildings. A rigorous preprocessing pipeline further yields two challenging benchmarks, \textsc{S80\_i30} and \textsc{S80\_i80}, derived from 160k real-world building samples.

Extensive experiments conducted on these datasets demonstrate the effectiveness of the proposed approach. Under moderate corruption (\textsc{S80\_i30}), the completion model attains an \textit{RMSE} of \textbf{0.89}~m and a Chamfer distance of \textbf{0.06}, improving upon the state-of-the-art RoofDiffusion baseline by 13.2\% and 17.3\%, respectively. In the severe setting (\textsc{S80\_i80}), the method sustains a 13.5\% \textit{RMSE} reduction. The upsampling stage delivers an additional 10\% \textit{RMSE} gain over the best classical interpolator, and the end-to-end pipeline achieves \textit{RMSE} values of 0.91~m (moderate) and 1.42~m (severe).

The thesis contributes: (i) a structurally-aware diffusion framework for roof completion, (ii) a scalable patch-based upsampler, and (iii) public benchmarks that reflect real lidar degradation. Collectively, these advances close a critical gap between theoretical research and practical generation of LOD2.2 building models, facilitating more reliable urban analytics and planning applications.

As urbanization and climate change intensify, managing the urban microclimate becomes increasingly challenging, affecting outdoor thermal comfort. The practical integration of urban microclimate research into urban design remains limited, in part due to the complexity and inaccessibility of existing tools. To support early-stage, climate-sensitive urban design at the neighbourhood scale, I present SOLWEIG For Design (SOLFD): a computation framework that builds on the existing SOLWEIG tool. SOLFD enables urban designers to visualize current microclimatic conditions and assess the impact of design interventions on outdoor thermal comfort. In particular, it focuses specifically on (in)direct solar radiation and its effect as quantified by the mean radiant temperature. Key contributions include: (1) extending SOLWEIG’s 2.5D model to a layered 3D representation for improved accuracy in complex urban geometries; (2) automating the data pipeline using open Dutch geospatial datasets; (3) enabling the modification of the existing urban scene; (4) enhancing output usability through temporally grouped mean radiant temperature maps, derived physiological equivalent temperature maps, and comparison statistics; and (5) significantly reducing simulation time with GPU acceleration. The accuracy of SOLFD was validated using sensor data, achieving an RMSE of 5.39°C. Underneath structures, the RMSE increases to 5.83 °C. The potential of SOLFD is further demonstrated with a case study across various Dutch urban typologies. By laying the foundation for an accessible decision-support tool for outdoor thermal comfort, SOLFD takes a step toward integrating climate-responsive strategies into the urban design process.

Standardising data formats for 3D city models is crucial for semantically storing real-world information as permanent records.
CityJSON is a widely adopted OGC standard format for this purpose, and its variant, CityJSON Text Sequences, decomposes large city objects into line-separated objects to enable streaming processing of 3D city model data.
However, the shift towards cloud-native environments and the increasing demand for handling massive datasets necessitate more efficient data processing methods across different platforms and on the web.
While cloud-optimised data formats such as PMTiles, FlatGeoBuf, Mapbox Vector Tiles have been proposed for vector and raster data, options for 3D city models remain limited.
This research aims to explore optimised data formats for CityJSON tailored for cloud-native processing and evaluate their performance and use cases.
Specifically, the study implements FlatBuffers for CityJSON, incorporating features like spatial indexing, spatial sorting, indexing with attribute values, and partial fetching via HTTP Range requests.
The methodology includes designing a complete binary representation of the CityJSON standard using FlatBuffers, conducting a comprehensive review of existing performance-optimised formats, and benchmarking their performance.
Successful implementation of this research will enable end-users to download arbitrary extents of 3D city models efficiently.
The research demonstrates that FlatCityBuf achieves superior read performance compared to CityJSONSeq while generally producing smaller file sizes.
The approach successfully encoded the entire Netherlands dataset into a single 70GB file containing both spatial and attribute indices, demonstrating scalability for national-scale applications.
For developers, the optimised format enables single-file containment of entire areas of interest, simplification of serverless cloud architecture, and accelerated processing by software applications.
Ultimately, this work improves the scalability and usability of 3D city models in cloud environments, supporting advanced urban planning and smart city initiatives.

This report documents the development of Delft3D, a web-based 3D application, designed to visualise Delft’s urban features—such as buildings, vegetation, and water bodies—using open datasets. Developed for the Municipality of Delft, Delft3D customises the Netherlands3D platform to provide accessible, interactive visualisation for residents and employees of the municipality. This report primarily focuses on the process of development and only explains the implemented features conceptually. After the explanation, we provide a concise reflection on the hurdles we faced during the project and we give an overview of the limiting factors.

By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.

To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.

The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.

The growing complexity of urban environments has intensified the need for accurate 3D city models to support simulations and analyses in various fields such as urban planning, energy demand, and computational fluid dynamics (CFD). However, these models often contain geometric and topological errors, such as non-watertight solids, intersecting volumes, and missing surfaces, limiting their usability. This thesis presents AUTOr3pair, an automatic framework designed to repair semantic 3D city models and address these issues.

The process begins with validating 3D city models using val3dity, which ensures geometric validity based on ISO 19107 standards. Errors are addressed hierarchically, starting with ring-level fixes and progressing to solid interaction-level corrections, focusing on localized repairs to minimize alterations while preserving geometric and topological integrity. Existing repair methods are integrated alongside new algorithms designed to meet the specific needs of different use cases, including visualization, energy demand estimation, solar potential analysis, and CFD simulations. Written in C++ for optimal performance, the framework supports CityJSON and OBJ. The repairs prioritize maintaining semantic consistency and minimizing data loss, but textures are excluded. This approach results in a robust validation and repair pipeline that generates detailed error reports and post-processing outputs, significantly improving the overall quality of 3D city models.

Extensive tests on real-world datasets, including 3DBAG tiles from Leiden and a dataset from Brussels, demonstrated that AUTOr3pair successfully repaired most geometric errors, raising validity rates to nearly 100%.
The framework achieved additional requirements, such as watertight geometry and proper surface orientation, for the specific use cases, CFD, energy demand, visualization, and solar power estimation. While some floating-point errors and geometric differences, due to global repairs, persist in complex cases, AUTOr3pair significantly reduces manual pre-processing and improves model suitability for various applications.

This thesis demonstrates that automatic geometry repair is feasible and essential for improving the quality and usability of 3D city models. It provides a foundation for further research and development, particularly in extending the framework to support more file types and refining its capabilities for additional use cases.

This thesis introduces the “city stack” framework and the concept of the “typology grid” to analyze and procedurally generate cities using publicly available geospatial data. It builds upon existing urban morphology research and combines existing metrics with new metrics to examine road networks and building footprints across 43 cities worldwide. The resulting unsupervised clustering of road patterns produced mixed outcomes: some global road typologies were consistently identified, while others lacked clarity or validity. Supervised classification of building typologies showed potential for city generation applications but faced challenges related to data quality and validation methods.
To encode the captured urban form, the typology grid was introduced, which allows for straightforward comparison between cities and links the analysis phase with the generation phase. While this approach simplifies complex urban patterns for better understanding, it may oversimplify details needed for effective city regeneration, needing future research into parameterizing the grid.
The simulated annealing optimization technique was applied to generate new city models to produce typology grids resembling those of actual cities. It proved a promising method, with relatively plausible results from just a few shape-based rules in the objective function. Computation time and grid size were a limiting factor, ruling out real-time use. The road and building generation methods based on the typology grid and city stack framework demonstrated the approach’s feasibility but indicated that further refinement is necessary.
Further contributions by this thesis are an open source command line tool for analyzing the urban form real-life cities based on publicly available geospatial data, a proof of concept tool for generating cities, and a publicly available dataset of the analyzed cities.
In conclusion, the city stack framework and typology grids offer a viable method for captur- ing and generating urban form and can be used as a starting point for future research.

While three-dimensional coral reef models are valuable for various applications, existing approaches like photogrammetric scanning and manual modeling require substantial time and expertise, limiting their scalability. Previous algorithmic approaches, particularly Agent-Based Models (ABM), have relied heavily on complex ecological simulations and deep domain knowledge. This thesis explores an alternative data-driven approach to automated coral reef modeling, investigating whether empirical data sources can provide a scalable method for generating ecologically plausible 3D models. Rather than simulating ecological processes from first principles, we develop a pipeline that leverages observational data to inform and constrain procedural generation techniques. Through systematic evaluation of available data sources, including the Global Biodiversity Information Facility (GBIF), CoralNet, the Allen Coral Atlas, the Coral Traits Database and the Smithsonian Institution's 3D coral collection, we identified both opportunities and significant limitations in current data availability. The research developed a modular pipeline implemented in Blender that combines procedural terrain generation with the placement of 3D coral models, integrating species occurrence data aggregated over geomorphic zones. To ensure robust data integration across sources and maintain compatibility with evolving taxonomic standards, the pipeline implements automated species name verification through the World Register of Marine Species (WoRMS) (WoRMS - World Register Of Marine Species, n.d.) API. While the resulting pipeline successfully establishes a foundation for automated coral reef modeling, limitations in available structural data necessitated the use of manually configured parameters for critical aspects such as terrain characteristics and population density. The pipeline's modular structure, standardized taxonomy handling, and integration with standardized classification systems position it well for future iterations as improved data sources become available. This research demonstrates the potential of data-driven approaches to coral reef modeling while highlighting the need for more comprehensive, fine-scale structural data to enable fully automated, ecologically plausible modeling of coral reef environments.

Roofing material classification is becoming pivotal in urban decision-making, supporting processes like asbestos mapping, disaster preparation, and urban heat island detection. However, research has mainly focused on a restricted set of materials. Additionally, many studies rely on expensive multi- or hyper-spectral imagery and often use outdated or ineffective classification methods, overlooking the transformative capabilities of advanced deep learning and data fusion. This thesis explores using a convolutional neural network for pixel-level classification of Dutch buildings by merging standard aerial images with LiDAR data, enabling detailed mapping and addressing concerns about classification efficacy relative to image- and object-level methods.

A framework was devised to generate a new semantic segmentation dataset with over 15.5 million pixels from 200 randomly selected images nationwide, covering eight distinct materials. To facilitate material identification in unfavourable lighting conditions, true-colour aerial imagery from the BM5 dataset was combined with rasterised features extracted from the national point cloud (AHN4), specifically reflectance, slope, and planar point density. Additionally, a quasi-normalised elevation model (nDRM) was employed, based on the corresponding digital surface model and median roof elevation of buildings in each scene, as provided by the 3DBAG dataset. The research was further investigated using the DeepLabv3+ semantic segmentation architecture with a ResNet-18 backbone, and the model was trained end-to-end on the generated dataset. In this context, a novel stratified splitting algorithm and weighting scheme to combat class imbalance in the training subset were introduced.

After thorough hyperparameter tuning, we achieved a 64.68% mean intersection over union on the test subset. Membranes and gravel outperformed almost every other study. However, there were notable confusion and omission errors with light-permitting surfaces and metal. Further testing of pixel-wise material maps' generalization to different LoDs of the 3DBAG considerably decreased gross errors. However, it might overlook some minor original predictions, thus not improving overall performance notably. Generally, LoD1.2 was inadequate for modelling multi-material roofs of different heights. While LoD1.3 improved this, it still missed small roof sections, unlike LoD2.2, which also had more outliers. Additionally, an ablation study on the LiDAR-derived component of the new dataset showed that removing slope and nDRM reduced performance by 10.31% and 8.61%, respectively, while density had the least impact. All ablated features were semantically linked, suggesting they should be combined into a single dataset.

The thesis showcases the relevance of pixel-based classification with DL and data fusion, providing resources for future research and indicating areas for dataset expansion and improved annotation.

The Earth's surface is a complex landscape that is essential for a wide range of applications, from urban planning to environmental monitoring. Digital models of the Earth's surface are generated through mathematical calculations using elevation data collected from various sources and the Digital Terrain Model (DTM) which captures the bare earth's surface topography in 2.5D. The creation of DTM is an approximation of terrain in unsample locations, by using x-y coordinates and one z value. Traditionally, terrain interpolation uses deterministic or geo-statistical methods to calculate elevation. This research would use random forest regression as an alternative method and to compare the results against traditional interpolation. Comparing different locations against traditional interpolation yields similar results overall. Feature importance, within the the points that are closest to the sampled ICESat-2 data point are more significant than other features used in Random Forest model. The correlation between these datasets and the spatial relationship established would impact on the results of the elevation. The improvement overall of using traditional interpolation compared to random forest regression is limited depending on the location and using model trained with local datasets. For model trained on other geographical locations, which shows similar differences.

The thesis explores the semantic understanding of urban textured meshes derived from photogrammetric methods. It primarily addresses three aspects with regard to urban textured meshes: 1) semantic annotation and the creation of benchmark datasets, 2) semantic segmentation, and 3) the automation of lightweight 3D city modeling using semantic information.

The first focus of the thesis is the development of a benchmark dataset to evaluate the performance of advanced 3D semantic segmentation methods in urban settings. An interactive 3D annotation framework has been proposed to assign ground truth labels to the urban meshes' triangle faces and texture pixels. This framework achieves efficient and accurate semi-automatic annotation through segment classification and structure-aware interactive selection. In the center of Helsinki, Finland, object-level annotations were made over approximately 4 km\(^2\) (including buildings, vegetation, and vehicles, etc.), and part-level annotations over about 2.5 km\(^2\) (including building parts like doors, windows, and road markings, etc.). The design of the annotation tools improves user operation and enables quick annotation of large scenes, while the resulting datasets allow researchers to refine their deep learning models for urban analysis.

Another research focus is on mesh segmentation algorithms. A novel semantic mesh segmentation algorithm has been introduced for large-scale urban environments, employing plane-sensitive over-segmentation combined with graph-based methods for contextual data integration. This approach, which utilizes graph convolutional networks for classification, significantly improves performance over traditional techniques based on our proposed benchmark datasets.

Finally, leveraging this semantic information, a pipeline for reconstructing lightweight 3D city models has been designed. This facilitates the automated reconstruction of CityGML-based LoD2 and LoD3 city models, ensuring high fidelity in geometric detail and semantic accuracy. The reconstructed large-scale, lightweight, and semantic city models significantly broaden applications in urban spatial intelligence, including automatic geometric measurements, interactive spatial computations, spatial analysis based on external data, and environment simulation using physical engines.

This thesis enhances the practicality of 3D data in real-world applications by utilizing semantic parsing of urban textured meshes to generate lightweight 3D urban semantic models, greatly enriching their usability. It also lays a solid foundation for future progress in understanding, modeling, and analyzing 3D urban scenes.

Geometric algorithms are usually designed based on the assumption of using arbitraryprecision representations. However, in modern computer systems floating-point arithmetic and finite-precision approximation are often used as implementing exact computations would require large computational resources and would be rather slow in the applications. Snap rounding (SR) is a widely used technique to transform representations of infinite precision into fixed-precision formats. It slightly modifies the original geometry of the input in order
to obtain a better organized geometric object arrangement and avoid the issues caused by applying floating-point arithmetic in geometric algorithms. The existing implementations of SR primarily focus on processing sets of line segments, while in practice polygons are of great significance as well yet has not received adequate attention so far. In this thesis, a new method is proposed to perform SR on 2-dimensional polygons. Firstly the input polygons are embedded into a triangulation. Subsequently, the triangulation is tagged with the ID information of polygons and the polygon boundaries are extracted and
stored in a container. The boundaries and the triangulation are then dynamically processed according to the identification of close polygonal vertices and close vertex and boundaries (under a predefined tolerance). After having snapped all the close elements the rounded polygons will be reconstructed from the processed boundaries. The testing results show the proposed method is capable of eliminating small gaps between elements (i.e. vertices and
boundaries) and is adaptable to various inputs. The topological and geometrical properties of polygons are preserved as much as possible. Finally, an overview of the limitations is provided, along with potential directions for future research.

3D city models are frequently used to acquire and store energy-related information of buildings to be used in energy applications, such as solar potential analyses and energy demand calculations. In this context, the most common data model is CityGML, which provides an application domain extension called the Energy ADE to store energy-related data in a systematic manner in XML format. On the other hand, CityJSON has been developed as a JSON-based encoding to exchange 3D city models, with the aim of eliminating the hierarchical structure and shortcomings of the XML-based CityGML. However, even though an extension mechanism exists in CityJSON, an energy-related CityJSON extension is not present in the current literature. Therefore, the aim of this thesis is to develop and test a CityJSON Energy Extension. To achieve this, the space heating demand calculation of buildings is chosen as the use case to validate and test the Extension.
In this thesis, a simplified version of the Energy ADE, called the Energy ADE KIT profile, is used as the first step to create a semi-direct translation to a CityJSON Energy Extension. After validating the Extension with the official validator of CityJSON, the space heating demand is calculated for a subset of the Rijssen-Holten in the Netherlands according to the Dutch standard NTA 8800. Required input data is collected from various data sources and stored in the CityJSON Energy Extension to test its usability for the use case. The Extension is then improved depending on the results of the tests based on the use case. The results show that the semi-direct translation lacked numerous objects and attributes to store certain input data, while the final version of the Extension fully supports the use case. Furthermore, while the semi-direct translation contained deep hierarchical structures, these were eliminated in the final Extension to comply with the design decisions behind CityJSON. The main differences between the Energy ADE and the CityJSON Energy Extension reflected this philosophy as well, where the former was built with a deep hierarchical structure, while the latter flattens this hierarchy by using the characteristics of JSON. In addition, a comparison in file sizes showed that the input 3D city model of the study area in CityJSON format had a file size of 40.6 MB, whereas the output CityJSON + Energy Extension file with all input and output data was 65.8 MB. It was discussed that this increase of 25.2 MB in file size is not significant, considering the high increase in the number of objects stored in the file. On the other hand, space heating demand calculation resulted in negative values for 32 buildings in the study area, which was not expected. While the possible reasons were detected, a solution could not be developed in the given time frame of the thesis. Overall, this thesis showed that the CityJSON Energy Extension can provide an easy-to-use alternative to CityGML Energy ADE, where the Extension files can be simply parsed by software and easily understood by the user without reaching large file sizes.

Building height information has been more used in a variety of industries in recent years. This information can be used in sustainable urban planning, urban climate research, population estimation, and three-dimensional (3D) building reconstruction, etc. Nowadays, building height information is obtained mostly by photogrammetry, high-resolution photos, and aerial Light Detection and Ranging (LiDAR) data. However, the existing technique is constrained by issues of scale, cost, and quality.
The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched in 2018, using photoncounting LiDAR technology to gather Earth’s surface elevation data globally. It represents the highest level in space-borne laser altimeters and has been proved can estimate building height.
In this thesis, a method is proposed to estimate the height of all buildings in the Netherlands. To estimate the building height, elevation data from ICESat-2 and footprints from Basisregistratie Adressen en Gebouwen (BAG) are two used datasets. Spatial interpolation methods and percentile methods are used to get ground and roof elevation for each footprint, respectively. Random Forest Regression (RFR) method is used to deal with the low coverage of ICESat-2.
The result shows that less 3% of buildings could obtain their height from ICESat-2 data. Among these buildings, 90% of them are lower than 10 meters and half of them are between 5 - 10 meters. This caused a low performance of the prediction model with Mean Absolute Error (MAE) of 2.1m. The building between 5 - 10 meters has the smallest MAE of 1.1267m. Small amount of available of ICESat-2 data is the key reason, which leads to the training data of building over 10 meters is not enough.
The results reveal it is impossible to get the height of all buildings in the Netherlands with ICESat-2 data. But the proposed method is a feasible option for buildings between 5 and 10 meters in height.

A 3D city model uses three-dimensional geometries to represent and model urban environments, in which the building model is the key feature. With the development of computer and data collection technologies, 3D city models are gaining growing capacities regarding storing rich information. This makes 3D city models more potentially useful than ever in the urban application domain.

When implementing 3D city models, the CityGML model is currently the most frequently used standard. It is being used by cities all over the world. CityJSON is an encoding for a subset of the OGC CityGML data model. It is a JSON-based data exchange format for digital 3D models of cities and landscapes, and it is easy for various operations. Due to the uniqueness of its structure, it is necessary to design corresponding data models to store CityJSON files in the database, to make querying and updating data within the database easy and convenient.

There are already some open-source solutions for storing CityJSON files in the database, 3DCityDB is one of them. 3DCityDB can store, manage and visualize data well, and it is open source. But the database design is very complex: for a tile of 3D BAG data, 3DcityDB uses a total of 66 tables to store data. The structure of the data model results in difficulties for database users to understand the imported data, and potentially leads to non-optimal operation performance when retrieving data for urban applications. Based on the drawbacks of the existing DBMS when dealing with the CityJSON data format, this project aims to develop a Postgres data model that can store CityJSON files simply and efficiently. The developed Postgres data model (CJDB) has a simpler table structure and data model design, a CityJSON data importer, and an interactive API user interface.

After going through this document (data model, importer and API section), the potential users will have the ability to:

• Import CityJSON files into a Postgres database,
• Perform queries on imported data,
• Perform operations using CJDB API.

In addition, by reading the benchmarking section, the users can gain an overview of the CJDB’s performance over the 3DcityDB’s.

The CJDB project is open-sourced, available on GitHub page.

The CJDB project will be potentially further developed by 3D geoinformation group of TU Delft and 3DGI.

Solar energy is an important renewable energy source that is already generated by millions of solar panels attached to building roofs throughout The Netherlands. Whether a roof is suitablefor solar panels relies on its type, orientation and whether it is put in shadow by a neighbouring object. This means, the higher the solar potential of a roof, the better.

A solar radiation model is used to determine the solar potential of a roof. Well-known Geographical Information Systems, such as ArcGIS and GRASS GIS provide solar radiation models for raster data. However, raster data does not model the 3D urban environment accurate enough. Vector data is better capable of representing 3D buildings models, but solar radiation models for vector data are not widespread available and are computationally inefficient, because of the shadow casting step.

This research aims at providing an efficient solar radiation model for processing large-scale 3D city models. The 3D BAG data set, containing all the buildings in The Netherlands as vector data, is taken as use case. Their building models are stored in CityJSON format, subdivided into smaller tiles based on the spatial extent. The implemented model in this research, called SolarBAG, takes one or multiple tiles as input, processes the building geometries to compute the solar potential, and outputs an enriched CityJSON file where each building roof consists of yearly solar potential values. Within the process, the building geometries are stored in an Rtree to allow fast retrieval when filtering neighbouring buildings that potentially cast a shadow over another building. The roofs of buildings are sampled into a grid of points to account for the variable solar radiation values on a roof surface caused by shadows of neighbouring buildings. A ray-box intersection method is used to find the neighbouring buildings casting a shadow on another building.

To assess the quality and scalability of the implemented solar radiation method, experiments are conducted. For the quality assessment, the solarpy module used to compute the beam solar radiation, is compared to ground truth values, and the solar radiation model is compared to the solar radiation tool in ArcGIS. For the scalability assessment, the solar radiation model is tested for an increasing number of tiles. Based on the assessments, it can be concluded that the implemented solar radiation model can successfully enrich building roofs with solar potential values for one or multiple CityJSON files. However, there are still some bugs and inconsistencies present in the solar radiation model, and performance gains could still be achieved by neighbour filtering improvements.

Data on the number of floors is required for a variety of applications, ranging from energy demand estimation to flood response plans. Despite this, open data on the number of floors is currently not available at a nationwide level in the Netherlands. This means that it must be inferred from other available data. Automatic methods usually involve dividing the estimated height of a building by an assumed storey height. In some cases, this simple approach limits the accuracy of the results. Therefore, the goal of this thesis is to develop an alternative method to automatically infer the number of floors.

Three different machine learning algorithms are tested and compared: Random Forest, Gradient Boosting and Support Vector Regression. These algorithms are trained using data on the number of floors obtained from four municipalities in the Netherlands. In addition, 25 features are derived from cadastral attributes, building geometry and neighbourhood census data. These features are tested in different combinations in order to determine whether a specific subset yielded better results. Furthermore, a comparison is made between features derived from 3D building models at different levels of detail.

The results show that building height, particularly 70th percentile height, is most related to the number of floors. Other 3D geometric features are also found to be quite closely related to the number of floors, specifically roof area and volume. However, a higher level of detail did not improve the results. Cadastral features are also found to be relevant; mainly net internal area and, to a lesser extent, construction year. Furthermore, models based on a combination of different features performed better than models based on single categories of features.

The best predictive model achieved an accuracy of 94.5% and a Mean Absolute Error (MAE) of 0.06 for buildings with 5 floors or less. This represented a substantial improvement on the results of the geometric approach, which had an accuracy of 69.9% and MAE of 0.31. However, above 5 floors, model performance was substantially lower. Machine learning provided only a slight improvement on the geometric approach for these buildings. In this case, the best model had an accuracy of 52.3% and MAE of 0.62, whereas the geometric approach was 47.5% accurate and had a MAE of 0.70. A comparison of the cumulative error distributions showed that the best model mainly improved the fraction of buildings that were predicted with an error of less than 1 floor. Overall, these results show that machine learning partially provided a better estimate of the number of floors than a purely geometric approach.

The volume and density of geospatial data is constantly increasing as newer acquisition techniques are developed to create higher-resolution datasets and more countries are creating country-wide datasets such as point clouds. However, methods to deal with this increase in the amount of data are lagging behind and the developments in computer hardware are no longer moving fast enough to compensate. Especially the ability to load and process datasets into the main memory of a computer is becoming more difficult. Therefore, to work with these massive datasets more modern techniques need to be used. In this thesis on one such method for dealing with these massive datasets is explored; streaming simplification of geometries.

Streaming of geometries relies on processing small portions of a geometric dataset at a time, only keeping as much information in memory as is necessary, instead of the whole dataset. This method ensures that memory bottlenecks are prevented and ever-increasingly large datasets can be processed. Processing done on a LIDAR dataset can range from creating rasterized surface or terrain models, to creating a triangulation. Triangulations are an imporant starting point for other analysis techniques such as watershed calculations, line of sight, or noise propagation. However, large datasets of high density lead to large triangulations which are still too complex for these techniques to work with.

To create more manageable triangulations it is necessary to simplify them, which means finding a subset of the vertices that best approximates the original surface. This can be done randomly, or feature-aware by taking into account the shape of the dataset that is being simplified and ensuring data is kept where it is relevant. The AHN3 dataset is so large that simplification cannot be done using traditional techniques. Therefore, to achieve simplification of the AHN3 dataset, this thesis focuses on the modification of an existing methodology for creating streaming triangulations by incorporating a simplification step.

The methodology implemented in this thesis consists of determining where a simplification algorithm can be introduced within the existing streaming pipeline, as well as determining which existing simplification techniques are applicable. After defining possible locations for a simplification module within the streaming pipeline, various methods are created and tested on different sizes of dataset. These methods are: random simplification, decimation, refinement, and a novel medial axis transform based approach. For the refinement method a number of different implementations are investigated.

After the initial results are analyzed, the decimation and medial axis transform algorithms are rejected due to extremely long processing times or a lack of accurate results, respectively. The refinement implementations are able to produce accurate simplified triangulations in a reasonable amount of time, where the best performing method hardly introduces a slowdown in processing at all. Using the fastest algorithm (FCFS) 4 billion points are processed in less than 11 hours, against 13 hours without simplification. Compared to other methods the accuracy of the methodology is similar for all of the approaches. However, the number of points that can be processed per second is less when compared to existing research. The best performing algorithm is a novel method defined as First-Come-First-Serve (FCFS), as it has the best ratio between vertex error and computation time.

The results of this research show that creation and simplification of a massive Delaunay TIN is possible and produces acceptable results, achieving RMSE's below 0.2m consistently. Despite simplification, the size of the resulting datasets is still often too large to fit within the main memory of a computer and will still require streaming processing to perform further calculations on larger areas. This suggests that with the current ever-growing datasets it is relevant to explore how all different types of calculations can be performed in a streaming pipeline, possibly allowing these to be chained one after another.

All methods created for this thesis are found here: \url{https://github.com/mdjong1/simpliPy}

With the increasing demand for 3D city models in various applications, providing efficient access to 3D city models on the web has become very important and valuable. However, there is a lack of web services in which users can directly and effectively access the city objects contained in the 3D city models on the web. Another problem with web services related to 3D city models is that the size of 3D city models can become very large. As a consequence, the browser will not respond immediately until all city objects in the requested 3D city model have arrived. Addi-tionally, although file-based systems are the easiest way of data storage, they have deficiencies in web services that require scalability, efficiency, and flexibility in data access. To fix these is-sues, the goal of this research is to develop the an efficient way of disseminating 3D city models on the web, to discover the method of enabling 3D city models to be parsed incrementally and efficiently by the browser, and to explore a proper database solution for storing 3D city models to support the fast data access.

CityJSON has advantages in web applications over the CityGML data format, thus being chosen as the main datasets used in this research. Inspired by OGC API – Feature, a RESTful API for fast access to geospatial features in CityJSON has been developed. The second part of my research is related to the data streaming. CityJSONFeature has been proposed to enable CityJSON to be parsed incrementally on the web. To improve the overall data streaming perfor-mance, I proposed two methods to stream the data from the database to the RESTful API so that the RESTful API can immediately start constructing the first CityJSONFeature and sending it to the user. The third part of my research is to explore a proper database solution for CityJSON datasets to best support the fast data access in the RESTful API. In addition, some auxiliary data is added into the database to improve the RESTful API’s capabilities with efficient data filtering and streaming.

To evaluate the efficiency of the implemented methodology, a systematic benchmarking has been performed on three aspects: 1) the database schema, 2) the two streaming methods, and 3) the performance difference between the DBMS and the file system. The results show that in most use cases the DBMS can better support the RESTful API than the file system with the help of the built-in query and index mechanism. In addition, the overall streaming performance is largely improved when adding data streaming from the DBMS to the RESTful API. Lastly, based on the benchmarks, an optimal database schema has been chosen to support the RESTful API with fast data access, efficient data filtering and streaming.