Utility networks are critical components of urban infrastructure, providing essential services such as water supply, electricity, gas, and telecommunications. The traditional method for mapping these networks is typically two-dimensional (2D) schematic representations rather than topographically and geometrically correct maps. These representations lack the capacity to convey the complexity and vertical intricacies of urban infrastructures. This limitation hampers comprehensive planning, efficient management, and risk mitigation during construction activities because 2D maps do not effectively represent the multi-layered and interconnected nature of urban utilities, leading to potential oversights and inaccuracies. This thesis addresses the challenge of enriching utility network data by integrating detailed data from utility trench surveys. These surveys provide precise positional and attribute information about utilities that are often missing in standard maps, such as the exact depth, spatial configuration, and physical characteristics of the utility lines.

Data from utility trenches in three Dutch cities—Enschede, Rotterdam, and Amsterdam—was acquired and analyzed. Methodologies were developed to extract, standardize, and integrate this data into existing utility network maps, enhancing their semantic content and spatial accuracy. The research demonstrated that integrating trench data can reveal inaccuracies in traditional utility network maps at the utility trench locations.

Key findings include the development of algorithms for extracting and processing utility trench data, the identification of common challenges between cities such as cable/pipeline labeling inconsistencies, and the comparison of enriched utility data and that of traditional utility networks. The research also highlights the importance of standardizing data models and the potential of three-dimensional models to provide a more comprehensive understanding of utility networks. A resulting recommendation was to improve data collection by including all information found and providing properly geo-referenced data.

Semantic 3D city models are essential for visualising, analysing, and managing the built environment. CityGML is an international standard for representing 3D spatial information with a Unified Modelling Language (UML)-based data model to address data heterogeneity and facilitate data exchange. Common formats for encoding CityGML data include Extensible Markup Language (XML), CityJSON, and relational databases, with PostgreSQL preferred for its spatial data management capabilities enhanced by PostGIS.
Although relational databases like 3D City Database (3DCityDB) support CityGML v.1.0 and 2.0, challenges remain due to complex schemas and the need for advanced Structured Query Language (SQL) knowledge to access nested features and attributes of the encoded CityGML data, especially through Geographical Information System (GIS) software like QGIS, which is widely used by Architecture, Engineering and Construction (AEC) professionals. To address these issues, the 3DCityDB-Tools plug-in for QGIS (plug-in) developed by the 3D Geoinformation group at TU Delft simplifies interactions with 3DCityDB-encoded data by providing a user-friendly QGIS interface, enabling the creation of GIS layers composed of unique feature geometries and associated with attributes. With the release of CityGML v.3.0 in 2021, 3DCityDB is being updated to version 5.0, requiring corresponding changes to the plug-in for compatibility.
This thesis investigates the changes in the CityGML spatial concepts and the differences in 3DCityDB encoding. The methods are derived based on the 3DCityDB v.5.0 structure, which consists of schema-wise scans for checking the existing feature geometries and attributes. The scan results are then stored in the metadata tables for users to select the desired feature geometries and attributes for generating GIS layers. In the implementation, feature geometries are determined by the inherited fixed spatial properties of space or boundary features. In contrast, the feature attributes are classified into four types: ”Inline-Single”, ”Inline-Multiple”, ”Nested-Single” and ”Nested-Multiple” according to the modified 3DCityDB encoding. Each type requires specific flattening (linearisation) strategies to be joined with the geometries. Finally, users can generate GIS layers by joining the queried feature geometries and attributes. Several query time performance tests are conducted to determine the method for storing query results and creating the layers.
The generated GIS layers demonstrate flexible access to the feature geometries and attributes with enhanced attribute management. The attribute flattening method facilitates the consumption of complex attributes, making them accessible for batch querying in QGIS. While direct editing of geometries and attributes in GIS layers is not yet supported, these advancements increase the usability of CityGML data. Coping with the XML complex feature schema is a persistent technical challenge in the GIS applications; the proposed approach provides promising alternatives that align with the ongoing development efforts in the QGIS community, offering a complementary pathway for handling complex geospatial data.

As living in urban environments increases, the impact of city design on the health of individuals becomes more relevant. Cities have shown to negatively affect individuals' circadian health. This thesis investigates the influence of façade and city lighting design on indoor circadian lighting availability, addressing the increasing prevalence of circadian disruption in cities caused by poor daylight access and excessive artificial light exposure.
The research introduces a workflow that evaluates how façade designs and urban lighting choices affect circadian light exposure within homes based on open data sources. By creating a tool capable of simulating these effects for individual floor levels on an urban scale, the study provides a method to assess and optimize architectural and urban design for circadian health with public information in a large-scale manner.
The results aim to bridge the gap between available 3D urban data and practical applications, offering design strategies that improve indoor circadian lighting availability and contribute to healthier urban living environments.
Key findings prove the misalignment of indoor circadian lighting availability with human needs in cities, and show the importance of window size, floor level and façade orientation in mitigating circadian disruption caused by the urban context. Next to that, it shows the negative impact city lighting has on indoor circadian health, for which design suggestions are done.

Diversity in the use cases of semantic 3D city models today is unprecedented. A key enabler for this is the CityGML standard developed by the OGC to facilitate storing and exchanging these city models. Nevertheless, CityGML only provides object definitions which cater for a wide range of applications, making necessary the need to attach additional semantic information specific to each domain. For this reason, CityGML was designed with generic components that allow it to be extended. Alternatively, an extensibility mechanism that strengthens semantic interoperability in data exchange is the ADE. An example is the Energy ADE which augments CityGML for Urban Energy Modelling at single-building and city-wide scales. Base CityGML datasets are commonly encoded using the XML, though there are other encodings based on the JSON and SQL. The latter encoding is favourable for its associated benefits that come from the underlying DBMS. The 3DCityDB , upon which this thesis is based, is one such encoding that is open source and developed for PostgreSQL and Oracle. It has a complex structure which makes it difficult for users without extensive knowledge of CityGML, databases and SQL to access data. Hence, the 3DCityDB-Tools plugin was developed to simplify user interaction with the 3DCityDB using QGIS. However, encoding an extended CityGML dataset in the 3DCityDB adds greater complexity to a system that is already complex. In addition, 3DCityDB-Tools currently has no support for ADEs. On this backdrop, this research was initiated to investigate the extent to which ADE support can be introduced to the 3DCityDB-Tools plugin. Its server-and-client-side components are further developed to have extended layers that interact with data in 3DCityDB tables, can be managed from the GUI in QGIS and whose attributes are editable. This was achieved in an incremental and iterative process while maintaining the current architecture and user experience of the plugin. Areas identified for future development relate to the underlying database encoding of CityGML and capabilities not yet supported.

This report details the development process of an open-data-based tool, an extension of the original interface created by Royal HaskoningDHV. The objective was to bridge the gap between geographical data and Architectural, Engineering, and Construction (AEC) industry applications. The tool aimed to transform spatial data for architects, facilitating contextual analysis in Rhinoceros and Grasshopper, ultimately aiding architects and engineers in enhancing designs based on environmental impact.

The initial tool focused on Netherlands data, but the ultimate goal was to make it applicable to other countries/regions. The research involved evaluating data availability for different regions, acquiring and aligning relevant data for Grasshopper, and implementing these data workflows into wind and solar analyses.

The data evaluation stage revealed challenges due to varying data availability and accessibility across countries. For example, Germany's fragmented data required navigating different portals, while Hong Kong's centralized data via API was more accessible. The lack of standardization hindered automation, necessitating manual data retrieval strategies that could be challenging for non-geomatics experts.

Data alignment methods varied, introducing complexities. For instance, Italy required 3D extrusion from 2D shapefiles, leading to unavoidable errors. Spain used a different method, showcasing the difficulty of a universal solution due to data standardization and interoperability issues.

Two techniques were envisioned for the open-data tool: TIN-based and Voxel-based methods, each with distinct qualities and limitations. The TIN-method offered high-quality analyses but required rigorous data alignment, while the Voxel-based method allowed flexibility but risked issues with resolution.

Limitations of exploratory analysis included a focus on five countries/regions and inherent constraints of Rhinoceros, limiting tool accessibility and requiring alternative approaches. Additionally, language barriers and data platform permeability might have led to overlooked datasets.

In conclusion, the report acknowledges the need for future work. Optimization of code for readability and performance is suggested, and the inclusion of additional data types (vegetation, land use, transport) in data workflows is proposed. Input from AEC professionals through methods like questionnaires or testing is recommended for further improvement. This report emphasizes the evolving nature of the tool and the importance of ongoing refinement to meet the needs of diverse AEC professionals.

In order to limit the global warming to well below 2 degrees Celsius, all sectors have to reduce their greenhouse gas emissions and become more sustainable. This also includes the building sector, which is in Europe responsible for 40% of the total energy consumption (European Commission, 2020). A way to work towards this goal is by retrofitting the existing building stock to become more energy efficient. Urban Building Energy Modelling (UBEM) can help in this endeavour by identifying energy-saving potentials and thus to effectively allocate the required resources (Horak et al., 2022). Yet, UBEM involves many stakeholders which is why standards are crucial to facilitate data exchange and interoperability among them. In this context, the Energy ADE v1.0 was developed as an extension for the semantic 3D city model standard CityGML 2.0. It serves two purposes, first by storing energy related information on the individual building level, and second by providing the necessary input data for UBEM simulations (Agugiaro et al., 2018). However, in September 2021 CityGML 3.0 was released. The introduced changes directly affect the structure of the Energy ADE, which is why it cannot fully function on it anymore. This thesis therefore answers the question, how and to what extent the Energy ADE for CityGML 2.0 needs to be adapted to be conformant with the new CityGML 3.0 standard. It is accomplished by following a model-driven approach, where the UML class diagrams for the mapped Energy ADE are created first, before automatically deriving the corresponding XSD schema file. Through the lossless mapping itself, the Energy ADE is integrated as much as possible into CityGML 3.0, while also maintaining a logical symmetry. As such it accounts for the introduced changes of CityGML 3.0, by making use of the space and geometry concept, the versioning possibilities as well as the provided structures to model time-dependent data. The result is eventually tested and verified by converting a sample dataset to the Energy ADE for CityGML 3.0. This work provides an example on how other ADEs can be adapted to fit the new CityGML 3.0 standard and thus hopefully to the further establishment of it.

Despite being relatively novel, generative adversarial networks (GAN) have already been appropriated for application to several problems within the field of architectural and urban generative design. However, the preceding GAN based models for building massing generation make use of only simplified and two dimensional representation of the built environment.

This work improves upon the existing deep-learning-based methods for generation of building massings and building group layouts, by fusing high accuracy three-dimensional building models with site context derived from cadastral and topographic data, sourced from openly available datasets in the Netherlands. Pix2pixGAN implementation in PyTorch, trained on existing massing data encoded into images as heightmaps, is used to generate building massing geometry. Two methods for geometry extraction from heightmaps are introduced, voxelization and vectorization. The goal for the model is to maximize similarity of morphological traits of configurations generated by the model to the ground truth training data. The effects of multiple proposed training configurations on the resulting massings generated by the model are evaluated, together with visual assessment, using their Spacematrix mappings.

Three distinct models with specific goals are presented - parcel infill model, street block infill model, and urban fabric infill model. All three models show a capability to learn spatial traits of existing building configurations and transfer them into new situations not encountered in the training data, which is confirmed by the distribution of Spacematrix mapping of the generated results being similar to the distributions of the ground truth data.

The proposed methodology represents a novel approach to generating building massing configurations by autonomously inferring the rules of their composition from existing urban areas. The resulting models could be used to provide initial states in optimization-driven design approaches, or as smart massing suggestion engines, assisting architects and city planners during the early building design process.

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.

Issues such as climate change, ecological conservation and sustainable energy have received a great deal of attention in the last decade. Studies have shown that cities are responsible for major energy use and waste emissions. In dealing with the growing environmental problems, people have to look to the cities they live in. Today, urbanisation is still accelerating worldwide which heralds a potentially huge opportunity to improve the environment by increasing the energy sustainability of cities.

To address the energy sustainability of cities, policymakers and urban planners are looking for ways to control energy consumption in buildings. Faced with a large number of urban buildings and complex climate factors, the measurement of building energy consumption has to be done with the help of relevant simulation software. Luckily, software and data formats associated with the calculation of building energy consumption have matured over the years through the efforts of academics and research institutions. This largely helps to solve the complex problem mentioned above. However, these elements still need to be optimized and improved. In this thesis, the research will focus on one of the urban energy simulation software CitySim, the 3D city database 3DCityDB and the 3D city model data format CityGML. Although it is currently possible to rely on these elements for urban energy simulation, the whole simulation process is complex. The main reason for this problem is the number of data extractions, data conversions and data storage required throughout the whole process. Also, the lack of proficiency in data formats, software usage and data storage can be a difficult problem for potential users. Therefore, this research will focus on developing a python-based interface to achieve the goal of well connecting the entire urban energy simulation process.

This approach simplifies the process of urban energy simulation from the preparation of a complete database related to urban energy simulation, to the full process of data extraction, conversion and simulation in python, to the final storage of simulation results back to the database. In addition to this, several user-friendly customised operations are also developed in python. In this way, I hope to help more users to conduct urban energy simulation analysis conveniently.

In the Netherlands, the coastal dunes are essential to protect the country against flooding. However, the rising sea levels increase the risk of flooding along these sandy shores. Moreover, due to a combination of human and natural activities, dune erosion has increased and will continue to do so in the coming decades. Besides flood protection, the Dutch shoreline is important for preserving biodiversity, the generation of drinking water and recreation. In recent year, the number of recreational buildings on the beach, such as bars and holiday homes has increased. This is relevant because previous studies show that such beach buildings affect the wind flow and limit aeolian transport of sediment towards the dunes.

This research studies the impact of beach house configurations on dune-ward sediment transport to limit the adverse effects on dune development. We use CFD simulations to study the wind flow around a 3D model of a beach with holiday houses, based on a section of the Noordwijk beach in the Netherlands. We implement the CFD software OpenFOAM to solve the RANS equations for turbulent, steady-state flow. The sediment transport that occurs is calculated using the wind direction and speed near the ground surface of the solution.

The study consists of multiple 3D models in which the placement of the houses is varied systematically, to study the effects of beach house configurations. Variations are made by rotating houses, individually or within a row, and changing the distance between the houses and dunes. We determine the annual effect on sediment transport by applying varying wind conditions based on historical wind data from Noordwijk.
As we have many simulations to run, and all need different parameters and settings, we automated the process. First, a PostgreSQL database is used to store all requirements for the CFD simulations, the metadata and the results the simulations give. Then, a Python script links the information stored in the database to the correct settings in OpenFOAM. This way, many simulations are run in a row, and the results area to compare.

The results show that rotating the houses individually towards the prevailing wind direction appears to improve the amount of dune-ward sediment that takes place, compared to beach houses placed perpendicular to the shore. Rotating a row of houses as a whole has a limited effect on the amount of sediment transport. However, combining the rotation of the row of houses and the houses individually towards the prevailing wind direction shows the best improvement in sediment transport. Changing the distance between the houses and the dune foot so that a row of houses forms a funnel shape pointing towards the dunes also yields promising results.
Because we use a simplified model and do not take factors such as moisture levels or fetch distance into account, the results of this study overestimate the amount of sediment transport that takes place and might not quite resemble the reality. Further research using scale models or wind tunnels is necessary to confirm the suggestions made in this thesis.

The emergence of spatial data, GIS-supported tools, web mapping technologies has opened up many applications for more inclusive spatial planning and spatial decision support approaches. Site analysis is strongly supported by spatial data analysis in both 2D and 3D, which offers a more comprehensive understanding of urban settings. Furthermore, 3D city modeling and 3D web technologies not only help visualize design scenarios but also promote communication among the stakeholders for better decision-making. The first version of a GIS-supported design tool for new urban development areas was developed in a previous study (which we refer to in this thesis as the “Buurt Generator”). The tool works with the Netherlands’ data context to assist the realization of the 3D models of urban development projects in an interactive computer environment. The pre-design stage of the tool was based on the semantic 3D city model with different urban KPIs stored in the 3D City Database. Template neighborhoods that match the development goals of the project were then selected to extract design KPIs. The design KPIs, together with the development goals of the sites, form the basis and guidelines for generating different scenarios in the design stage. The scenarios are then integrated back into the 3D city model and visualized in 3D and are disseminated via web platforms. This thesis aims to test, critically review, and propose extensions and improvements for the “Buurt Generator”. It starts with a general review of the tool and literature reviews on related concepts and technologies. Then, the thesis investigates the accuracy of the generated 3D City model in estimating buildings’ volumes. Since volumetric measurements play a critical role in deriving urban KPIs and design KPIs, their accuracy is highly concerned. Hence, a volumetric comparison approach with other existing 3D city models is employed. The second focus is on the expansion of urban KPIs and design KPIs. The work bases on a data-driven approach that considers spatial and non-spatial, volumetric, and non-volumetric urban parameters. Moreover, the thesis proposes a comprehensive understanding of the city context and the project site based on available data. Then, it would be the task of the urban practitioners to reason the design KPIs for the new urban development project. The third focus is to develop a framework to study the impacts of the design solutions on the urban tissue. The framework is developed chiefly based on integrating the design into the 3D city model to perform (spatial) analysis. One of the energy-related criteria – the solar radiation factor - is chosen for further elaboration. The thesis contributes to the further integration of 3D city models into the urban planning process and explores its possibilities in assisting urban practices. It confirms the usability of the generated 3D model in estimating buildings’ volumes. It expands the list of urban KPIs and assists the information query to understand the city context and extract specific information. It bridges 3D City Database and Grasshopper for post-assessment of designs regarding solar radiation and opens the way for other urban simulations.

The plastic pollution of aquatic environment is undoubtedly an emerging environmental risk, as it negatively affects ecosystems globally to a great extent. To prevent the plastic soup from growing even further, a Delft-based start-up Noria has developed plastic collectors, to remove plastic from rivers and canals before it reaches the ocean. In order for these devices to give maximum positive effect, they need to be installed in areas where plastic is more likely to accumulate - the plastic hotspots. Taking into consideration various natural attributes that affect the movement of the plastic waste in the water, such as wind direction, water flow, canal geometry, vegetation and man made structures in waterways; potential hotspots can be predicted in a model which would allow more efficient coordination of the cleaning process. Thus, this project aims to locate plastic accumulation zones in the city of Delft in a (semi-) automated manner using open spatial data analysed in GIS and a network simulation model.
The methodology developed in this project results in the visualisation of potential plastic hotspots where Noria’s collectors could be placed in order to remove and recycle the plastic. The potential hotspots suggested by the model were compared with ground truth data collected. The final result yielded only 20% accuracy and therefore did not meet the initial expectation. An evaluation of the shortcomings was made with suggestions for future research.

In the Global South, large urban spaces resulted in the duality between the so-called ‘formal’ and ‘informal’ cities. It is the case of São Paulo, a twenty-two-million people metropolis and a financial hub in Latin America. Albeit a vast literature addresses the social-spatial segregation emerging from this dual built environment, the scarcity of spatial datasets regarding informal settlements also enforces a geo-information segregation, resulting in a terra incognita. This is exemplar in favelas, defined as precarious, spontaneous, and unorganised land occupation built on third-party property, most of which lack cadastral data. Since favelas are often not mapped, assessing urban phenomena becomes a technical challenge for several application domains, e.g., the energy one. A recent public initiative in Brazil estimates solar irradiation and photovoltaic potential for buildings at city scale, but favelas are intentionally excluded from the resulting web-based solar maps. Technicians believe that the absence of a spatial pattern in favelas calls for investigation on how to refine a roof mapping methodology. The main research question becomes: “How far is it possible to perform solar analysis on buildings of favelas in São Paulo, with the goal of estimating PV Potential?”. The research is structured into two topics: 1) Roof Mapping, which investigates the data pipeline that leads to a digital reconstruction of favelas; 2) Solar Irradiation, which investigates how existing solar irradiation modules – GRASS GIS, ArcGIS, CitySim, SimStadt, Ladybug and the one developed by Virtual City Systems – perform when assessing buildings of favelas. From a roof mapping perspective, the experiments reveal that the absence of cadastral datasets represents a complex technical challenge. Nevertheless, the reconstructed and post-processed building footprints cover the extension of the cadastral footprints that are available for the study area, with building shapes that are satisfactory as a first approximation. Regarding the solar irradiation perspective, qualitative and quantitative analyses are carried out to compare the results coming from the six solar modules. The qualitative analysis indicates that each solar module offers potentialities but also limitations. Therefore, a straightforward choice is not possible, since the optimal solution will be derived from a data-driven approach that considers, among other factors: the scale of the favela, its topographical characteristics, the presence/absence of urban features other than buildings (such as vegetation), a possible pre-selection of buildings of interest, etc.; The quantitative analysis reveals that ArcGIS outputs an annual summation of irradiation values that is the closest to the one offered by the meteorological station of São Paulo, adopted as ground truth. Nevertheless, from an accuracy perspective, CitySim outputs a daily curve that best corresponds to the ground truth one. In conclusion, based on the geometrical model and the weather dataset criteria, the author expresses his preference for a raster-based solar module – GRASS GIS or ArcGIS – if, on the one hand, the reconstructed building footprints result in an unrealistic or excessively complex vector-based model. On the other hand, if the resulting vector-based model is simple enough and representative of the built environment of the favela, the author suggests the adoption of CitySim.

Urban energy simulation is becoming more and more important in various areas like urban planning, architecture design and city management. It provides quantified insights for architects and governors to deliver energy-efficient approaches. There are already quite a few energy simulation software or engines on the market. Among these tools, Ladybug family, a series of open-source python packages have its advantages: easy to use, high level of customization and low cost of adoption. It could be run in Rhino Grasshopper, a visual programming interface widely used by architecture industry. Taking 3D geometry created in Rhino and local weather data, Ladybug tools (Ladybug and Honeybee) prepare simulation recipes to run energy simulation with validated software engines like EnergyPlus and OpenStudio. With all these advantages, when using Ladybug and Honeybee for urban energy simulation, there are two major flaws: it is tedious to build 3D models of all building blocks in Rhino one by one and many key parameters required by energy simulation have to be entered manually. This geometry creation and parameters entering process could be avoided when using CityGML data as input, as CityGML with EnergyADE data already has 3D geometry and energy-related attributes of city within its data model. In this research, a mapping table between required simulation parameters of Ladybug tool - Honeybee and CityGML with EnergyADE data model is created. Based on this mapping relationship, by following a database approach, all information stored in CityGML with EnergyADE data is retrieved and stored in tables of 3DCityDB and later queried in Rhino Grasshopper and used in data mapping and processing workflow. Energy simulation results could be saved back to database too. It is concluded that using CityGML with EnergyADE data as input for Honeybee tools is applicable as there is a sufficient mapping relationship between their data models. However, as Honeybee has certain restrictions on input geometry (shared surface areas should be independent surfaces and surfaces should be convex etc.) and it runs energy simulation of buildings not simultaneously but one by one, it is not efficient to use Honeybee for large scale urban energy simulation.

Recently, the application of machine learning and data fusion techniques on hyperspectral imagery have demonstrated potential for ground cover classification at material level. Hereby, specific locations of resources enclosed in cities (e.g. roof materials) can be identified, which is critically relevant within the field of urban mining. A limitation of this approach is the so-called 'pepper and salt effect', the oversensitivity of the classifiers to spectral variations within a pixel (e.g. chimneys, roof windows). Identifying and correcting affected pixels can be done statistically (e.g. using a majority filter), but not in cases where spectral variations affect a majority of pixels characterizing a surface. A solution to this limitation would be the usage of 3D city models containing the objects inducing the spectral variations. However, such highly detailed 3D city models are often unavailable as they cannot be produced automatically yet. An alternative covered by this research is to use a less detailed 3D city model and semantically enrich it with the required data. As 3D city models are usually produced using a point cloud, such a point cloud is used to perform the enrichment. The main research question addressed is therefore: How can a CityGML LOD2 model be semantically enriched in order to improve material classification performed on roof surfaces?.      To address this, an existing LOD2 model was compared to a point cloud acquired by Ligth Detecation and Ranging and 'deviation' points were identified. This identification uses a distance check for seed selection and performs a region growing with an orientation check. In a subsequent step, 'deviation' point regions were translated into a geometric shape by usage of their Voronoi diagram and fused with the pixels of hyperspectral imagery. Part of this research is also a nominal validation analyzing a total of 41 buildings and 831 pixels located in the south of Rotterdam (Netherlands). Overall kappa values of up to 0.7 and commission errors as low as 10% (for the class 'clean' pixels) were obtained, showing potential of the chosen method. Additionally, a rational validation was performed to assess the impact of potential tolerance of classifiers for 'spectral deviations'. This one only included 10 buildings, but took into account 328 pixels located up to 30% outside the roof surface A main outcome is the recommendation on settings to use depending on the specific user needs. To accurately quantify materials, relatively 'loose' settings are recommended. In contrast, to identify presence of materials, stricter settings are recommended. Beyond this, recommendations to data suppliers and potential applications of the method to other fields are formulated.

During the MSc Geomatics program at the Delft University of Technology, the students execute a synthesis project. The project aims to investigate a real-world problem and to bring students and teachers/supervisors in cooperation with a company or public agency (e.g. municipalities). The students are challenged to use their existing knowledge in geomatics and to gain further experience in handling of geo-information as to come in touch with the involved stakeholders. The duration of the project is ten weeks and takes place at the end of the first year. The novelty of this project is to establish an automated method to assess buildings in
Amsterdam on their suitability for catering services, based on the compliance of their chimneys to minimum height restrictions.

In urban planning, one of the common units of measurement for population density is the amount of households per hectare. However, the actual size of the households is seldom considered, neither in 2D nor in 3D. This thesis proposes a method to calculate the average size of the households from existing urban areas based on available open data and to use it as a design parameter for new urban developments. The proposed unit of measurement is comprehensive of outdoor and indoor spaces, the latter comprising both residential and non-residential space. As a test case, the Haven-Stad project in Amsterdam was chosen, specifically its second phase, Sloterdijk One, envisioned by the municipality of Amsterdam to host 11220 households and 7480 working places by 2040. The sizes of typical households, as well as a series of other KPI’s (Key Performance Indicators) were computed in different neighbourhoods of Amsterdam based on their similarities with the vision of Sloterdijk One. In the second part of this thesis, the resulting size of the households were used as a design parameter in a custom-made prototype tool to generate semi-automatically several design proposals (called scenarios) for urban developments. Additionally, each scenario can be converted into a Geo format to visualize it in web-based virtual platforms such as Google Earth and Cesium JS. Significant differences among the resulting design proposals based on this new unit of measurement were encountered, meaning that the average size of a household plays indeed a major role.

In recent years, the need to reduce the global warming of the planet has become more imperative than ever. Global warming and, at local scale, Urban Heat Island phenomena are among the primary effects of the increased building carbon emissions. Nevertheless, understanding and controlling the parameters which intensify, or mitigate, the increasing in temperatures in the surroundings of a building are pivotal, as sustainable design can significantly reduce the buildings’ energy demand. Micro-climate simulations can provide more accurate input for building energy simulations since they can accurately simulate the interactions between those parameters to calculate detailed weather data. Despite the increasing knowledge about the significance of the microclimate, energy simulation users still rely on derived, or interpolated weather data from sparsely located weather stations, located generally outside the urban environment. The reason behind this commonly adopted approach is that the generation of microclimate data is costly in terms of time, and currently standards for storing this generated data have not been developed. ENVI-met is a microclimatic simulation software that requires a model of an urban area and weather parameters on its boundaries to generate a large extent of data like air, temperature, relative humidity, wind speed etc. Constructing this model manually contains a number of significant limitations, such as high design cost in time and need for data collection from different sources – thus the chance of design errors is high. In this thesis a novel approach is introduced where the ENVI-met software is used for microclimatic simulations at district scale. However, the input model in this case is created by data extracted from a CityGML-based 3D city model. In addition, the generated microclimatic data is stored back to CityGML, where it can be re-used. The proposed methodology is implemented via a Graphics User Interface, divided in two main phases, serving the required bi-directional data flow. It was designed and implemented based on the following specifications: i) the user involvement in the whole process needs to be minimum, ii) the interface should create simulation-ready input models of various resolutions and iii) it must work with different CityGML datasets.
A data requirement analysis indicated that a CityGML-based city model can feed its data to ENVI-met by the interface, so that the input model required by ENVI-met can be constructed fully automatically. In return, the storage of the generated results data is also possible. Therefore, an automated data flow between a CityGML-based city model and ENVI-met can be achieved, offering the following advantages: i) the ENVI-met input model can be constructed fast and automatically and ii) ENVI-met outputs can be translated to real world coordinates – thus can be visualized and processed in GIS software and ultimately stored back into the CityGML-based 3d city model.

To cut down immense greenhouse gases emission and energy consumption in the rapidly urbanizing world, a holistic understanding and rethinking of our dynamic urban energy system are inevitable. Performing bottom-up building energy modelings at urban scale based on Geographic Information System (GIS) and semantic 3D city models could be a promising option to provide quantitative and integrated energy solutions.

Nevertheless, input uncertainties either caused by limited data accessibility in most cities or parameters with stochastic variability (e.g. house occupancy profile) become one of the biggest obstacles to produce reliable and acceptable building energy modeling results. This study aims to address the heating demand simulation performance gap caused by input uncertainties. In this case study based on Amsterdam residential building stock, parameter importance ranking of the 14 simulation inputs are first derived according to the sensitivity analysis. The selected key uncertain parameters are then modeled in a probabilistic distribution way at postcode 6 level (approximately or slightly more than 10 buildings). Model calibration is based on the Bayesian approach and given six years (2010-2015) of gas consumption data to infer parameter posterior distributions. After the training phase, the calibrated annual heating demand simulation results of the validation years show significant improvement in modeling accuracy. Comparing the baseline and the calibrated simulation results, the averaged absolute percentage errors of energy use intensity (EUI) among at least 84 valid postcodes have decreased from 24.96% to 8.31% in 2016 and from 19.93% to 7.70% in 2017 respectively.

The calibrated urban building energy model would be most interested by municipalities, urban planners, and engineering consultancies. It can be used to evaluate long-term energy supply and demand strategies, identify building renovation saving potential, perform large-scale building performance mapping, and carry out retrofit measures assessment.