The Netherlands is facing a housing demand of 1 million homes before 2030. Most of these residences are planned to be built in and around existing cities, causing an increase in urban densities with sub-optimal indoor daylighting conditions as a result. Simultaneously, the daylight assessment methodology for buildings in the Netherlands is set to change from the Dutch NEN 2057 to the European EN 17037. The European norm uses more accurate metrics to express daylighting performance but does not consider urban context (i.e. external buildings) in the simulation models. As a result, a concern is that indoor daylighting in dense urban areas is inadequately protected. Moreover, it is unknown to what extent the urban context affects the well-being of humans, regarding visual and non-visual levels of daylight. A multitude of daylight simulations is run and analysed in the thesis to better understand the impact of the urban context on indoor daylighting performance. Visual daylighting is assessed following the EN 17037 methodology with urban context integrated. Non-visual daylight performance is assessed using two novel metrics: melanopic autonomy and melanopic isotropy. The results have revealed that the discrepancy between simulations with and without the integration of urban context is up to 90% for realistic residences throughout the Netherlands, depending on urban characteristics and density. On average, indoor daylighting is decreased by 36% when the urban context is integrated with the EN 17037. The non-visual stimulus was found to be sufficient in residences that are compliant with EUmin levels but insufficient for residences that only comply with the Dutch building code. Sky view factor (SVF) and Building Floor were found to be useful indicators of daylighting performance in early design stages. Urban density indicators such as the FSI and OSR seem to be negatively correlated with daylighting performance. The thesis concludes with the advice to include urban context in daylighting simulations so that bad daylighting can be properly mitigated. Effective mitigation strategies are increasing glass transmission values, interior reflectance values, and exterior building reflectance values. Another effective strategy is to avoid bad daylighting conditions in the first place by not positioning residences on the first 5 building floors in high-density urban areas. The results from this thesis can be used by daylighting designers and architects who are interested in ensuring adequate and healthy daylighting conditions in the residences they design: not only in digital environments but in the real world.

*Public version of internship report* Urban wildlife plays an invaluable role in cities, including the promotion of overall biodiversity and greenery, pollination and human connection to nature. This internship project involved the development of a prototype that quantifies the potential for local ambassador species to visit a building site based on the amount and connectivity of habitat in the neighbourhood. The workflow integrates vector layers from open datasets and Arup ecologists’ expertise on animal behaviour in order to quantify the cost of moving through an urban environment. Initial results show that the prototype enables the numerical and visual comparison of connectivity for 7 out of the 10 ambassador species, with the Bee demonstrating the highest connectivity, and Toad having the lowest connectivity. This prototype has the potential to support Arup’s work on facilitating more connected urban environments for local wildlife, all the while improving urban ecology overall.

Due to global warming, the Netherlands is experiencing a variety of climatic changes, including temperature rise, increased solar radiation and condensation, low pressure, high humidity, wild-fires, drought, subsidence, changes in ground-water levels, an increased risk of flooding, and downbursts, thunder, wind gusts, and hail. Build-ing materials, including steel, concrete, and timber are affected directly or indirectly by these climatic events. When it comes to resilience, increasing moisture, temperature, subsidence, and flood dam-age affect structural materials most. Investigating the impact of temperature and flood damage on construction materials was the main goal of the thesis. For flood assessment, flood loads based on the FEMA coastal construction handbook are used to simulate flooding damages and evaluate the impact on structures. Hand calculations are used to calculate the deflection. Damage evaluation involves calculations from reference cases and utilizes databases such as Hazus, the REDi rating system, and FEMA to determine recovery and repair times. The script for the assessment tool incorporates these calculations, formulas, and numbers. Eventually resulting in a graph based on the dimension of the column and the material, the deflection, damage, recovery time and repair time is returned. The current flood assessment is limited to deflection in terms of structural assessment, but it can easily be expanded to contain stress calculations or other likewise formulas. Regarding the temperature impact, an empirical concrete corrosion formula calculates mass loss, while the Arrhenius equation assesses the deterioration of wood and steel. A specific formula for concrete corrosion considering the concrete layer is required. Using Faraday's equation, corrosion ratios or material degradation ratios can be converted into mm/year, determining the new cross section size and assessing its impact on structural deflection by anticipating the mass loss. This loss is also directly linked to the structure's performance in a flood, effectively integrating both investigated events. Both flood assessment and temperature effect approaches are translated into a Python script using packages like open-meteo for climate data, klimaateffectatlas for flood depths, and ifcopenshell for data extraction from IFC models.

Current urban tree inventories rely heavily on time-consuming manual work and often fail to capture all trees. To effectively monitor the impact of urban trees on their environment and vice versa, an automated method for detecting and grouping trees based on their characteristics is crucial. This research aims to expand current urban tree inventories and cluster trees based on their characteristics. Existing inventories typically include species, age, and height, but trees of the same species and age can vary significantly due to environmental factors. This study focuses on a 500x600 meter area in Delft, encompassing 641 recorded trees from the municipal inventory. An automatic tree detection method using airborne LiDAR (AHN4) point cloud data combined with Random Forest classification was implemented, achieving an accuracy of 85-90%. Individual trees were identified using an existing tree segmentation algorithm, detecting 70% of recorded trees with a mean location difference of 0.71 meters and identifying an additional 460 trees, including those on private land. Despite promising results, limitations include the undetection of small trees (below 3 meters) and classification errors leading to missed detections, multiple identifications for single trees, and false positives. Geometric and reflectance features were extracted. Highresolution, 30cm, spectral images from the SuperView Neo satellites, acquired across three seasons, provided spectral features like tree color and NDVI. Overall resulting in a total of 50 features. This comprehensive inventory allows for clustering of individual trees based on geometric, reflectance, and spectral similarities using a K-means algorithm. The approach enhances urban tree inventories by incorporating new features from airborne Li- DAR and spectral images, such as tree height distribution, crown sphericity, and density. Seasonal changes from spectral images provide insights into tree behavior. These detailed features significantly improve clustering, effectively grouping similar trees together. The findings reveal that trees of the same species in seemingly similar environments exhibit significantly different characteristics. This research offers a method to enhance urban tree inventories and supports long-term studies to reveal how trees respond to urban development, climate change, and ecological dynamics. Correlating tree growth and health with specific locations and climatic conditions can aid in developing sustainable urban planning and conservation strategies.

Current urban tree inventories rely heavily on time-consuming manual work and often fail to capture all trees. To effectively monitor the impact of urban trees on their environment and vice versa, an automated method for detecting and grouping trees based on their characteristics is crucial. This research aims to expand current urban tree inventories and cluster trees based on their characteristics. Existing inventories typically include species, age, and height, but trees of the same species and age can vary significantly due to environmental factors. This study focuses on a 500x600 meter area in Delft, encompassing 641 recorded trees from the municipal inventory. An automatic tree detection method using airborne LiDAR (AHN4) point cloud data combined with Random Forest classification was implemented, achieving an accuracy of 85-90%. Individual trees were identified using an existing tree segmentation algorithm, detecting 70% of recorded trees with a mean location difference of 0.71 meters and identifying an additional 460 trees, including those on private land. Despite promising results, limitations include the undetection of small trees (below 3 meters) and classification errors leading to missed detections, multiple identifications for single trees, and false positives. Geometric and reflectance features were extracted. Highresolution, 30cm, spectral images from the SuperView Neo satellites, acquired across three seasons, provided spectral features like tree color and NDVI. Overall resulting in a total of 50 features. This comprehensive inventory allows for clustering of individual trees based on geometric, reflectance, and spectral similarities using a K-means algorithm. The approach enhances urban tree inventories by incorporating new features from airborne Li- DAR and spectral images, such as tree height distribution, crown sphericity, and density. Seasonal changes from spectral images provide insights into tree behavior. These detailed features significantly improve clustering, effectively grouping similar trees together. The findings reveal that trees of the same species in seemingly similar environments exhibit significantly different characteristics. This research offers a method to enhance urban tree inventories and supports long-term studies to reveal how trees respond to urban development, climate change, and ecological dynamics. Correlating tree growth and health with specific locations and climatic conditions can aid in developing sustainable urban planning and conservation strategies.

Balancing the requirements of urban planning with the need for rapid and efficient iterative design, while accurately assessing its detailed impact on existing surrounding buildings at a per-room level, presents a significant challenge in architecture design, the conversion and integration between multiple platforms introduce additional complexity, establishing high standards for designers to achieve. Integrating Building Information Modeling (BIM), Geographic Information Systems (GIS), and per-room daylight simulation into a unified workflow offers a promising solution. This thesis develops a web application that leverages Grasshopper(GH) scripts for backend processes, Rhino Compute as the intermediary API, andMapboxGL as the GIS-based front-end platform, enabling seamless user interaction with BIM data for customized daylight simulations.Since the advent of digital tools, architects have sought ways to enhance and streamline the design process. Integrating these technologies bridges the gap between urban planning and indoor daylight simulation, facilitating real-time interaction and data manipulation. Grasshopper scripts are developed to compute Aperture-BasedDaylight Modeling (ABDM) metrics, Sunlight Beam Index (SBI), and sky lumen, providing a foundation for evaluating their relationships with traditional Daylight Modeling (CBDM) metrics like Daylight Autonomy (DA), Spatial Daylight Autonomy (sDA), and Useful Daylight Illuminance (UDI).This research explores the potential of combining these advanced tools to create an interactive web application that allows users to manipulate building typology, location, scale,façade materials, and levels of detail (LOD) for surrounding city models. The study demonstrates a robust linear relationship between SBI and the daylight metrics DA, sDA, and UDI, with room geometry playing a crucial role. Skylumens, however, show limited effectiveness as a CBDM alternative.The thesis concludes that MapboxGL is a promising platform for integrating BIM models into room-based daylight simulations, offering an intuitive user interface for urban planners and designers. Rhino Compute technology ensures smooth data transfer and interaction, significantly enhancing the workflow. The study results show that SBI has a linear relationship with traditional CBDM metrics. However, multiple moderating variables affect this relationship, and it does not apply to north-facing rooms. Thus, SBI is not a current substitute for traditional CBDM metrics. Overall, this thesis provides a comprehensive framework for integrating BIM, GIS, and daylight simulation into a cohesive system, paving the way for more efficient urban planning and design practices.

Balancing the requirements of urban planning with the need for rapid and efficient iterative design, while accurately assessing its detailed impact on existing surrounding buildings at a per-room level, presents a significant challenge in architecture design, the conversion and integration between multiple platforms introduce additional complexity, establishing high standards for designers to achieve. Integrating Building Information Modeling (BIM), Geographic Information Systems (GIS), and per-room daylight simulation into a unified workflow offers a promising solution. This thesis develops a web application that leverages Grasshopper(GH) scripts for backend processes, Rhino Compute as the intermediary API, andMapboxGL as the GIS-based front-end platform, enabling seamless user interaction with BIM data for customized daylight simulations.Since the advent of digital tools, architects have sought ways to enhance and streamline the design process. Integrating these technologies bridges the gap between urban planning and indoor daylight simulation, facilitating real-time interaction and data manipulation. Grasshopper scripts are developed to compute Aperture-BasedDaylight Modeling (ABDM) metrics, Sunlight Beam Index (SBI), and sky lumen, providing a foundation for evaluating their relationships with traditional Daylight Modeling (CBDM) metrics like Daylight Autonomy (DA), Spatial Daylight Autonomy (sDA), and Useful Daylight Illuminance (UDI).This research explores the potential of combining these advanced tools to create an interactive web application that allows users to manipulate building typology, location, scale,façade materials, and levels of detail (LOD) for surrounding city models. The study demonstrates a robust linear relationship between SBI and the daylight metrics DA, sDA, and UDI, with room geometry playing a crucial role. Skylumens, however, show limited effectiveness as a CBDM alternative.The thesis concludes that MapboxGL is a promising platform for integrating BIM models into room-based daylight simulations, offering an intuitive user interface for urban planners and designers. Rhino Compute technology ensures smooth data transfer and interaction, significantly enhancing the workflow. The study results show that SBI has a linear relationship with traditional CBDM metrics. However, multiple moderating variables affect this relationship, and it does not apply to north-facing rooms. Thus, SBI is not a current substitute for traditional CBDM metrics. Overall, this thesis provides a comprehensive framework for integrating BIM, GIS, and daylight simulation into a cohesive system, paving the way for more efficient urban planning and design practices.

Due to safety or preservation reasons, certain objects or areas need to be inspected regularly. Currently, the inspection of objects is mostly done in-person, which is labour-intensive and not very effective. With the use of drones, areas and objects can be inspected from new angles at a much faster rate. To effectively monitor these objects with drones, the position and location needs to be extracted from drone data in real time. In this thesis, a case study is done on the localisation of fisher boats in restricted areas. Several components are integrated to create a prototype. The pretrained YOLOv3 detection model is trained on acquired nadir boat images, which makes it able to predict the bounding boxes of boats on images captured with drones. A positioning algorithm is constructed, which calculates the geographical coordinates from the pixel coordinates for images taken both in a nadir and an oblique angle. A real time connection is constructed between the drone and the prototype. This is done by creating a connection with Google Drive with the drone controller and the prototype. The positioned polygon bounding boxes are localised using a real time dashboard, which visualises the bounding boxes in a map with other relevant layers. The results indicate that the performance of the components and prototype as a whole are satisfactory for this use case. To deploy this prototype in other object localisation use cases, it is recommended to train the pretrained model further, use a drone with more accurate equipment and run the prototype on the drone controller.

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient grid expansion is becoming increasingly difficult. Therefore, deep learning (DL) techniques, such as convolutional neural networks (CNNs), can support collecting meta data about PV systems from aerial or satellite images, as research in the field of remote sensing has shown. However, previous research lacks the consideration of ground truth data-specific characteristics of PV panels. This thesis aims to implement a semantic segmentation model that detects PV systems in aerial imagery to emphasize the relevance of area-specific characteristics for the training data and convolutional neural network (CNN) hyperparameters. A CNN with U-Net architecture is employed to analyze the impacts of land use types, rooftop colors, near-infrared (NIR) data, and lower-resolution images on the detection rate of PV panels in aerial imagery. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial images (10 cm) by reaching F1-scores of up to 91.75% while demonstrating the importance of adapting the training data to area-specific ground truth data in terms of urban and architectural properties.

Computational Fluid Dynamics (CFD) simulations for residential areas are becoming increasingly important as the urban population keeps growing and extreme weather conditions due to climate change are becoming more prevalent. Since CFD simulations simulate and visualise fluid flows, users can analyse and predict air flows in urban environments, giving insight in pollution, heat dissipation, and weather conditions. Performing these simulations demands expertise and time: 3D urban models must be prepared, and pre-run setups must be defined to obtain accurate results. Partial automation can streamline this process. Therefore, we developed a method that identifies geometric errors and defines mesh parameters for the open source CFD software OpenFOAM. This method follows the ISO19107 standard and recent CFD guidelines for urban areas, ensuring accurate simulation results. We validated algorithms with a variety of urban models and parameters, and analysed the workflow developed for the mesh parameters definition. Additionally, we created a prototype, in the form of a web application, in which these algorithms are implemented. Our prototype will simplify the use of CFD simulations for urban areas, making them more accessible to everyone.

Nowadays, the evolution of localisation and navigation technologies is vast, aiding towards facilitating users’ guidance in various environments. Outdoor positioning can be easily achieved, with the widely used Global Navigation Satellite Systems (GNSS), which comprise a universal standard for positioning and are included in every person’s mobile device. However, due to the presence of high buildings in dense urban environments and bad reception in indoor environments, the performance of GNSS is significantly degraded. Therefore, alternative ways of positioning and localisation respectively, need to be explored. In indoor environments, unlike outdoors, there is no universal standard, as the different indoor localisation techniques, that are currently implemented have their own bottlenecks. The most widely used Wi-Fi fingerprinting, requires a constantly up-to-date radio map of the signals from the Wi-Fi access points, whose creation is also a heavy and time-consuming technique. Additionally, other techniques require an installation of costly sensors or either equipment. Therefore, this thesis investigates the possibility of the ceilings in public or semi-public buildings, being used for indoor localisation, by using features that are included in a simple mobile device. The research additionally involves location tracking of different users, in order to discover different movement patterns in an indoor facility. Indoor localisation is achieved based on the comparison of user and reference data, that can be both point clouds and images, using the Light detection and ranging (LiDAR) of an iPad 12 pro and camera sensors of an Android device. The point cloud-based localisation is implemented based on different combinations of global and local registration techniques, while the image-based approach involves different feature detection, description and matching techniques. Using a web-application to visualise the indoor localisation results, an indoor model and a network graph of the Faculty of Architecture and the Built Environment, location tracking of different users is implemented and visualised in a heat-map. Additionally, a dashboard is created that can be used by a facility manager to translate the user paths to valuable information and reveal different movement patterns in an indoor facility. The followed methodology showed promising results, concerning the reliability of ceilings for real-time indoor localisation, based on LiDAR and camera sensors, that are incorporated in up-to-date mobile devices. The robustness of Colored Iterative Closest Point (ICP) algorithm for indoor localisation based on point clouds was revealed, both in terms of time efficiency and quality, while the combination of Speeded-Up Robust Features (SURF) feature detector and Scale Invariant Feature Transform (SIFT) descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergency situations, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations.

Urban areas are home to over half of the global population and consume significant natural resources, necessitating effective city management for urban resilience and sustainability. Resilient cities are better prepared to face challenges, from natural disasters to economic downturns, and adapt to climate change, resource scarcity, and population growth. Urban digital twins, which are virtual replicas of cities integrating data from various sources, play a pivotal role in enhancing and supporting urban resilience. These digital twins empower urban planners, designers, and the public to make informed decisions, identify vulnerabilities, and respond to acute shocks in real-time. Leveraging technologies like the Internet of Things (IoT) and 3D city models, urban digital twins offer a dynamic representation of the city’s functions, allowing for better visualization, monitoring, and decision-making. However, challenges related to interoperability and data acquisition need to be addressed to maximize the potential of urban digital twins. This thesis investigates how environmental sensor data can be collected, processed, and integrated into 3D city models to visualize the dynamic elements of a city comprehensively. It explores the use of international standards, such as the OGC SensorThings API standard to acquire, store, and manipulate real-time and historical crowd-source, sensor data in a consistent and interoperable manner. The concepts of sensor data pre-processing and interpolating are also explored through OGC standards, in the context of providing detailed spatio-temporal information for the urban environment, while the results are visualized in a 3D web application.

Nowadays, humans rely in technology more and more when it comes to navigation and localisation and in many aspects of life as well. While most concepts related to localisation and navigation of outdoors environments are already well derived from various researches and softwares, the indoor environment remains a significantly unexplored area. Nevertheless, lately there have been increased interest on Location Based Services (LBS) and Indoor Positioning Systems (IPS). There are already several methods available for indoor localisation such as Wi-Fi Fingerprinting and Bluetooth Beacons, but none of them is fully functional yet. It remains a field that requires more and further research and investigation in order to reach a satisfactory and complete Indoor Localisation-Navigation method. Therefore, this thesis's main objective is to investigate and explore a new method for Indoor Localisation based on Isovists. The exploration and evaluation of Isovist-Fingerprinting approach for Indoor Localisation can extend the fields of LBS and Geomatics. The main research question is “To what extent can isovist support Indoor Localisation” and through this and a series of sub-questions to analyse the Isovist concept in relation to the Indoor Localisation. This is achieved by forming a proof of concept and a methodology that investigates how the Isovists would benefit an LBS. To succeed that the methodology is divided into 4 main sections. The Data Acquisition for which the newly supported from smartphones Light Detection And Ranging (LiDAR) technology were used. The Space Syntax and Isovist Analysis Measures, where all the concepts related such as the Isovist Parameters were analysed in depth for better understanding of their effect. Then the Matching and Localisation Algorithms, where the possibilities and options on how to reach the localisation were investigated and analysed. And finally, the Tests and Experiments took place in order to evaluate all the prior stages of the methodology. The main conclusion of this research is that a method for Indoor Localisation based on Isovists is feasible and can indeed support an LBS. The analysis and evaluation of all related components has be done and if putting all the parts in the right order they can be of high value for LBS applications. Since is a new method of Indoor Localisation, there is plenty of future work to be done which mainly focuses on how to connect it with existing techniques and integrate all together into a user application.

The production of cocoa beans contributes to 7.5% of European Union (EU) driven deforestation. For this reason, the recent European Union Deforestation-free Regulation (EUDR) requires producers to perform comprehensive tracking of cocoa farm extents. However, cocoa crops present unique detection challenges due to their complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous work employs Multispectral Imagery (MSI) and/or Synthetic Aperture Radar (SAR) for pixel-based classification of satellite images. Convolutional Neural Network (CNN)s offer a promising approach to semantic segmentation of cocoa parcels that considers = both spectral and spatial characteristics. This thesis aims to evaluate the impact of combining SAR and MSI data in the training of a CNN for cocoa detection, in order to demonstrate the importance of texture, moisture and canopy characteristics in identifying cocoa canopies. A U-NET is employed to evaluate how prediction results are impacted by the stacking of MSI datasets with different SAR polarizations, seasons and temporality. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62%. This research demonstrates the influence of SAR measurement season and polarization, and ground truth classes, on the semantic segmentation of cocoa.

There is a drive to create an inclusive open data ecosystem, that includes public, private and academic open data [Loenen et al., 2021]. There is a lot of existing research in Open Government Data [Janssen et al., 2012; van Panhuis et al., 2014; Martin et al., 2013] and plethora of OGD in the Netherlands. Although the private sector produces a lot of data, those are not open, as they are not participating in data sharing. In order, to fill the existing data gap, that OGD create, the EU [Commission, 2018], specifically with the Open Data Directive, started to promote more openness in private sector data, especially geospatial data. So far this, it is only done for public undertakings, as a sector in between public and private Boone and van Loenen [2022]; van Veenstra and van den Broek [2013]. The private sector is not bounded by legislation to share their geospatial data. This research aims to identify what are the challenges to arrive at an ecosystem with more open private sector data, through the identification of the barriers in the process, in relation to the level of openness they are and how to move forward. Five categories of barriers are identified, strategic, technical, legal, economic, cultural and a multi model with 4 levels of openness is used, to identify the current state of geospatial data sharing of 9 companies in the Netherlands. The results show that companies that are providers of data are mostly sharing internally, trying to share with external users, while companies that are intermediaries are mostly sharing with some external users but they are not sharing fully open data yet, and the companies as user of existing open data are difficult to identify. This research demonstrates the influence of the role of the company in data sharing and the level of dataset, project, department in the barriers that the private sector faces and prevent them from sharing geospatial data in the Netherlands.

With increased urbanization and the impacts of climate change, cities around the world are making resilience-building a priority. Simultaneously, advances in technology have enabled the creation of City Digital Twins (CDTs). Informed by interviews with four resilience and digital twin experts, this paper explores how CDTs might support the development of more resilient urban communities. First, the various definitions of CDTs are described. Second, the paper explores how characteristics of CDTs make them uniquely equipped to facilitate (1) a better understanding of complex phenomena, (2) the imagination of possible futures and (3) collaboration between stakeholders. Finally, the technical requirements and challenges of CDT implementation are discussed, including (1) identifying priority hazards and users, (2) collecting and managing data, (3) integrating different models and (4) ensuring usability. The paper concludes by emphasizing the important role of stakeholders in shaping CDTs that can be successfully integrated by the communities they serve.

There has been an ever increasing focus on the development of smart cities, both by big corporate in the field of Information Communication and Technology as well as respective governments responsible for these cities worldwide. This in combination with the influx of easily accessible data owing to cheaper and efficient sensors has risen to the concept of digital twinning of urban spaces and cities. Urban digital twins require the city to be represented in 3-dimensional digitally, these datasets are oftentimes too large to be provided on as-is basis for most platforms. To tackle this problem of large 3D datasets, the OGC 3D tiling specifications were formed and accepted in 2019. This thesis attempts to develop a neighbourhood designing platform which would enable users to design neighbourhoods in 3D while being able to experience the design in an immersive manner by utilizing virtual reality’s capability to observe 3D assets in scales which are not possible using a physical model or a 2 dimensional neighbourhood plan, employing the OGC 3D tiling technique to understand the extent to which a combination of existing 3D datasets can be made with a user-interactive neighbourhood designing platform in a VR environment. The results of the study reveal that using our methodology, it is possible to combine existing 3D datasets with user made neighbourhood design with their own 3D assets of any level of detail and furthermore, it is possible to utilize instanced tiling format to disseminate the neighbourhood design as a standard 3D tiled design.