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.

Plant phenotyping plays a vital role in plant genetics and breeding programs, providing the foundation for screening and evaluating genetic diversity and linking phenotypic parameters to the genetic determinants of trait expression. This process is critical for identifying molecular markers and accelerating genetic breeding improvement, thereby enhancing plant resilience to biotic and abiotic stresses such as drought, salinity, and diseases. Recent advancements in 3D sensing technology have empowered researchers to extract precise phenotypic parameters from plant point clouds, enabling more detailed and accurate plant phenotyping. A critical step in point cloud-based plant phenotyping is plant organ segmentation. Among the available segmentation methods, skeleton-based approaches are simple and intuitive, and could leveraging both local and global geometric information from plant point clouds to facilitate accurate organ segmentation. These methods have gained considerable attention in recent years. However, plant skeletonization, the core component of these approaches, remains limited in handling leafy plants, especially herbaceous species with complex shoot architectures, lateral stems, and multiple leaves. These structural complexities pose challenges that current skeletonization techniques struggle to address effectively.

To address this challenge, we propose a skeleton-based plant organ segmentation framework that accurately extracts curve skeletons from individual plant point clouds and performs precise stem-leaf segmentation based on these skeletons. Our framework is particularly effective in handling leafy plants. It preserves fine structural details during skeletonization while avoiding abnormal or noisy local branches by extending the Laplacian-based contraction (LBC) algorithm through the integration of the Constrained Laplacian Operator. Moreover, we introduce Adaptive Constraints and Tip Points Preservation within the contraction loops to further refine skeleton quality. Additionally, a modified Locally Optimal Projection (LOP) operator is utilized to perform skeleton points calibration, ensuring that the extracted skeleton is centrally aligned with the original plant shape. Furthermore, to evaluate the performance of our proposed framework, we contribute a photogrammetric 3D plant point cloud dataset of 56 Polygonum lapathifolium plants, complete with detailed annotations. Experiment results demonstrate that our framework robustly handles various shapes and sizes of leafy plants and tree branches.

In conclusion, our study enhances the LBC algorithm by integrating the Constrained Laplacian Operator, Adaptive Constraints, and Tip Points Preservation. These improvements increase the accuracy and quality of curve skeleton extraction from leafy plant point clouds, enabling satisfactory plant organ segmentation.

Creating GPS art on the map is an interesting way to make one’s outdoor activity more engaging. Cyclists, runners and hikers can create impressive drawings on the map by traversing the road/pedestrian network in a carefully planned way. Such planning, however, is often tedious and time consuming, which makes the GPS artists have to meticulously design the routes with the complex road network in mind. The aim of our research is to come up with a full process that can express a person’s initial idea (for example a contour drawing) as a route, which the user can then follow to create their GPS art. This involves transforming the input image to match the routing network in a selected area and generating a route which approximates the shape in the best possible way. In our work, searching for patterns in the road network is cast to an image matching problem with template matching as the solution. Generating routes is achieved using a graph routing algorithm with a custom cost function, to make the resulting route as similar to the input shape as possible. Finally, two ways of generating artistic routes are presented. First is an automatic GPS art workflow, which attempts to find an optimal initial location of the route, then generates a number of candidate routes and selects the best one according to various evaluation criteria. The second method is an interactive browser application, where the user can select an initial location for his shape on the map, move, scale or rotate it and get instant feedback in the form of artistic routes displayed in real time.

In 2018, a new standard was released for daylight in buildings: EN 17037. This is the first standard for daylight in buildings for Europe. In the Netherlands it will replace the NEN 2057. The determination method for daylight provision for EN 17037 is based on internal illuminance. Additional ambitions for the design quality factors: view, direct sunlight and the prevention of glare have been added to the standard. A sustainable design is all about balancing daylight performance and energy consumption. Large windows will cause the building to overheat and use too much cooling energy. Too small windows will result in low levels of daylight availability and visual comfort. For this reason, it was still unclear whether it is possible to achieve these new recommendations for daylight and at the same time meet the energy requirements for Green building certificates, such as BREEAM and LEED. The main aim of this research was therefore to investigate how much influence the European standard has on the energy consumption of a typical office building and whether the requirements for BREEAM and LEED can still be met.
The analysis shows that the European standard has an influence on the daylight and energy credits for BREEAM and LEED. For the daylight credits this standard has a positive influence. For the minimum performance level for daylight provision for the European standard this is not yet enough to meet the requirements for daylight provision for BREEAM and LEED. For BREEAM and LEED, the variants only meet the requirements if the medium and high recommendation levels for the European standard are met. However, the ASE is often too high for a design that meets the high performance level for EN 17037. It should also be taken into account that when a certain performance level for daylight provision has been achieved for the European standard using method 2 (based on internal illuminance per hour for a typical year) this same variant will usually score lower with method 1 (based on daylight factors).
The energy consumption goes up when the recommendation level for the European standard is higher. When comparing the average energy consumption of variants with the minimum performance level for the European norm with the high performance level, the total energy consumption goes up 8.33 kWh/m2 . These values can be higher or lower with different orientations. To reduce the negative impact of the European standard on BREEAM and LEED, certain parameters for the design can be chosen differently. The most important parameters for meeting the high-performance level and minimizing primary fossil energy consumption is the window-to-wall ratio and width/depth ratio. To find a balance between daylight and energy consumption, the optimal width/depth ratio is between 1.33 and 0.75. The optimum window-to-wall ratio is between 40% and 60%. To reduce the cooling consumption and ASE even more, designers can consider the introduction of overhangs in the façade, or other interventions that reduce the window SHGC.

Semantic segmentation of aerial images is the ability to assign labels to all pixels of an image. It proves to be essential for various applications such as urban planning, agriculture and real-estate analysis. Deep Learning techniques have shown satisfactory results in performing semantic segmentation tasks. Training a deep learning model is an expensive operation, while most of the time manually labelled images are required. Additionally, a bottleneck in semantic segmentation projects concerns the annotation of images. Consequently, synthetic data, which consists of images from a virtual world that simulates the real world, can be used as training data for segmentation tasks to improve the classification results. Therefore, this thesis aims to create a pipeline that generates synthetic images with semantic segmentation labels to be used in an existing deep learning model and discuss how the generated synthetic data improves the semantic segmentation of aerial images. In this research work, an existing model (FuseNet), which in previous works achieved satisfactory results, is trained with solely synthetic data and a mix of real data in different training and testing scenarios to classify true ortho imagery from Haaksbergen, Netherlands and Potsdam, Germany. In addition, a benchmark of domain adaptation techniques is performed to close the domain gap between the synthetic and real imagery. The semantic maps include building, road and other classes. Experiments are performed to test the performance of the synthetic data using 1) Different 3D models of the virtual world, 2) Different quantities of synthetic and real training data, 3) Different cross-geographical scenarios, and 4) Different domain adaptation techniques. The assessment is based on the (mean) intersection over union (IoU), F1 score, precision and recall and an extensive visual assessment. The virtual world is created through a pipeline in CityEngine using procedural modelling techniques and then rendered in Blender to create the training dataset. The results show that the synthetic data has a mIoU of 0.48, which is lower compared to cases when solely real data (0.75) are used, when the segmentation is performed in the same training and testing area. In addition, the 3D models partly affect the segmentation results. When using a mix of real and synthetic data, the results are maintained to a mIoU of 0.75. On the contrary, when training and testing in different areas, the use of synthetic data seems to improve the results on average by 21.5, 12.5, 1.5 and 2 percentage points on the mIoU, IoU for classes building, road and other respectively. Additionally, domain adaptation techniques such as Cycle GAN and Cycada improve the performance of synthetic datasets by 4 percentage points. Overall, this thesis shows that when the domain difference between the training and testing datasets is big, the addition of the synthetic data helps to improve the performance of the semantic segmentation of aerial images. Synthetic datasets improve the segmentation results by using a mix of existing labelled imagery from different geographical regions when a project lacks labelled imagery. In contrast, when labelled imagery is present in the same testing area, the real training data obtains robust results, thus the addition of synthetic data does not improve the segmentation results.

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.

In 2016, a collaboration between the TU Delft, RIVM and a few other companies started to automate the generation of environmental input data for noise modelling.
As a part of that cooperation a promising triangle based data structure is researched to represent terrain elevation.
Due to the properties of its generation it can provide certainty over the presence of objects while the format allows for efficient cross sections extraction.
Yet a persistent challenge remains, the large file size makes exchange and processing of terrain data troublesome.
This research aims to optimize the terrain elevation data to decrease the file size while ensuring accurate noise calculations.

Using an in-depth analysis of the usage of terrain elevation in noise modelling in a Dutch and European context, a environmental based approach for a triangulated terrain generation is developed.
This approach is conceptually implemented to facilitate the calculation of noise values using terrain generated with the provided approach.
Empirical tests have been conducted to measure the influence of different terrain elevation settings over a variety of terrains.

The study concludes by providing an advice on an approach for elevation model generation.
Several insights are discovered regarding the impact of terrain elevation accuracy on noise propagation calculations.
Compared to a currently publicly available terrain data set a size reduction of 50 to 90% can be obtained without quality reduction.

This research concerned the design and implementation of a system to perform the 3D conversion of a national road network using airborne Lidar data and land-based road elevation measurements, preserving its topology and quantifying output elevation accuracy. While the system was designed with generality in mind, specific Dutch open data datasets were used as input which were also used in a commercial project with the same goals. In addition to designing a scientifically sound equivalent of the commercial implementation, this research also explored purely academic goals, such as selecting road surface laser reflections accurately and using them for 2.5D road surface modelling purposes. Our system was based on decomposing the 3D conversion task into individually solvable 2.5D sub-problems to enable the use of efficient terrain modelling methods, and to prove that scaling via multiprocessing would be possible. At the 10-30 points per m² sampling density we worked with, the road surfaces are oversampled. We found that because our elevation estimates are based on road reflections only, and because road surfaces are smooth and relatively flat, the output quality and accuracy depends only on the specific technique used to extract the elevations from the Lidar data. Using our methods, the output elevation accuracy represents a 30% increase relative to the Lidar input data, amounting to 10.6 cm at 95% certainty with our specific datasets. This is assumed to be violated only where local density drops below 3 points per m², which occurs where roads are occluded by opaque objects, unless the land-based road surface measurements can provide enough additional data. Elsewhere, more than 98% of the road network conforms with the minimum point density. The 2.5D-based decomposition was found to be an effective means of simplifying the problem and terrain modelling methods were successfully used in the procedure. More than 90% of the traffic-occupied road surfaces are included in the resulting surface models, and about 75% of the total paved surfaces. We extracted the output elevations from them, using linear interpolation where they were unavailable. Our 3D conversion quality and accuracy represents an improvement relative to the commercial results, mostly due to the better prioritising of the input elevation sources, in addition to our solution's capacity to handle complex small to medium-scale occlusion better, including complex relationships between roads such as multi-level motorway junctions.

Bathymetric Airborne LiDAR technology is used to map the depth of water bodies. It uses a green light sensor which is able to penetrate the water surface and reach the bottom part of the interesting water areas. However, water conditions affect the capability of the green laser penetration. Factors such as the water clarity, the water turbidity (waves) and the vegetation are some of the crucial restrictions for green light to penetrate the water; particularly in shallow inland water areas. This research examined the capability of green LiDAR data to improve the bathymetric surveys in case of muddy and shallow inland Dutch water bodies. The potential of green LiDAR increases as the monitoring of water depths is getting easier, faster and more efficiently in terms of cost than manual GPS measurements. The main challenges of this thesis are concentrated both on the existence of various sparse and dense parts in the point-cloud and on the limitations of the data in terms of quality due to the not ideal water conditions. Specifically, this thesis presents a workflow with required procedures that aim to process a raw green LiDAR point clouds of water bodies and then classify them into three classes: water surface, underwater and bottom points. Pulse and Neighbourhood based algorithms were implemented in order to perform a classification process with high level of automation. Point characteristics such as intensity, number of returns, return number were analysed per pulse. Voxelization was used as a spatial method to divide the 3D space into water columns (3D Voxels). The spatial distribution of the water points into the water columns was examined based on different factors such as elevation, density, intensity. By comparing and partially combining those methods the detection process was improved to deal with shallow and muddy water bodies. A classification confidence value was calculated and stored for each potential bottom point. The resulting output is a classified green LiDAR point cloud based on the confidence values. Using elevation, density and confidence values, raster DTMs with multiple bands were created for each water body. To sum up, this thesis proposed an efficient workflow to process and automatically classify green LiDAR water-body data using both voxel and pulse based methods.

Semantic segmentation (or pixel-level classification) of remotely sensed imagery has shown to be useful for applications in fields as mapping of land cover, object detection, change detection and land-use analysis. Deep learning algorithms called convolutional neural networks (CNNs) have shown to outperform traditional computer vision and machine learning approaches in tackling semantic segmentation tasks. Furthermore, addition of height information (Z) to aerial imagery (RGB) is believed to improve segmentation results. However, discussion remains on the following: to what extent height information adds value; the best way to combine RGB information with height information; and what type of height information can best be used. This study aims to answer these questions. In this research work, the CNN architectures FCN-8s, SegNet, U-Net and FuseNet-SF5 are trained to semantically segment 10 cm resolution true ortho imagery of Haarlem, potentially augmented with height information. The outputted topographic maps contain the classes building, road, water and other. Experiments are conducted that allow for the comparison of 1) models trained on RGB and on RGB-Z, 2) models combining RGB and height information through data fusion and through data stacking, and 3) models trained using different types of absolute and relative height approaches. Performances are compared based on scores on the performance measure (mean) intersection over union (IoU) and through visual assessment of outputted prediction maps. The results indicated that on average segmentation performance improves by approximately 1 percent when absolute height information is added. The class building showed to benefit the most from the addition of height information. Furthermore, extracting features from height information in a separate encoder and fusing these into RGB feature maps, led to a higher overall segmentation quality than when height information is provided as a stacked extra band and processed in the same encoder as the RGB information. Finally, models using relative height delivered a higher quality segmentation than when absolute height approaches were used, especially for large objects. The best performing model; FuseNet-SF5 trained on RGB imagery and pixel-level, relative height, retrieved a mean IoU of 0.8427 and IoUs of 0.8744, 0.7865, 0.9131 and 0.7966 for the classes building, road, water and other respectively. This model was able to correctly classify over 90% of the pixels of 67% of all the objects present in the ground truth. Overall, this study showed that, when considering semantic segmentation of aerial RGB imagery, 1) height information can improve segmentation results, 2) adding height information through data fusion can result in a higher segmentation quality than when data stacking is used, and 3) providing relative height to a network, rather than absolute height, can improve semantic segmentation quality.

Automatically generated 3D city models are becoming less of a futuristic, demanding or even impossible to attain goal, and more of a necessary, or vastly sought after, means for a multitude of applications. The current prevalence of open geographic information, such as nationwide-covering LiDAR datasets in the Netherlands, opens up opportunities for different parties to experiment in a search for solutions based on LiDAR data. A current approach in answering this demand for 3D city models is 3dfier, which is an ongoing project to automatically generate, disseminate and maintain a 3D city model based on open source airborne LiDAR datasets as a main source. Trees are currently not included in the 3D city models generated by 3dfier, while trees are an integral part of any city landscape.

In this thesis, an implementation is developed that goes through multiple stages of the construction of 3D tree models. First, an initial classification method of the available LiDAR point cloud data is done. This results in a new intermediate point cloud that consists of mostly points belonging to trees. These classified tree points need to be segmented, such that each segment consists of a group of points that represent a single tree. A second classification is constructed after the segmentation, which is called data cleaning. This step ensures that every segment that consists of tree points, is checked for misclassifications and outliers and that these are removed. After cleaning every segment, tree models can be constructed in various LODs and additionally, the types of trees are classified based on identifying features of these trees.

The conclusions of this research are that it is possible to construct 3D tree models based on airborne LiDAR point cloud data and that these can be made to fit in an existing 3D city model. This is demonstrated by creating a 3D city tree model for an existing 3D city model and merging them into one dataset. While further work is required to achieve a seamless fit, the integrated results show that the datasets complement each other well.

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.

In the last two decades there has been a steady rise in the gathering and use of 3D geo-information. A common way to store and use 3D data is by using 3D city models. In 3D city models, geo-information can be stored at different levels of detail. CityGML, the most commonly used data model and encoding for 3D city models, uses five levels of detail in order to model increasing geometric and semantic complexity. These levels of detail may be interpreted as a model quality measure, and as a guideline for users that need data for a certain application. CityGML consists of several thematic modules, each with their own level of detail specification. Some of these modules have a more further developed level of detail specification than others. Recently, several authors have proposed improvements to the Transportation module. This has led to proposals for various changes, especially concerning road data. However, these proposed changes have not been encoded yet. The main critiques are the lack of a level of detail specification for linear representations of roads, no ability to model networks, no representation of intersections and general ambiguity in the level of detail specification. Many road data use cases might potentially benefit from improvements on these points. In this thesis I attempt to improve the current level of detail specification of roads in the CityGML data model. The improvements are encoded in CityJSON, a JSON encoding of this data model. I assess the shortcomings in the current CityGML transportation module. After, a road data needs analysis is performed on three use cases: transport modelling, navigation and road maintenance. The data needs are compared to modelling approaches of other road data standards. This has resulted in several encoded improvements. A topological structure has been added to CityJSON. This includes the addition of two new modelling classes: Nodes and Edges. This structure is general such that it can be used by other thematic modules as well. Moreover, the level of detail specification for roads has been further developed to include both the linear representations and less ambiguous areal representations. This includes a prescription on how to model intersections and roundabouts at different levels of detail. Finally, I provide a structure which enables one to link the linear and areal road data together. This link is made at the scale level of the object, which data providers may choose themselves. This way one object can be modelled in two representation types without needing a one-to-one mapping between linear road segments and areal triangulated surfaces. These concepts are then tested by creating a CityJSON road data file for all new levels of detail.

The needs and interests for 3D data based on large-scale topography are increasing. A wide variety of these 3D data needs have emerged in multiple domains and for many different applications. Though, research have shown that the interoperability of this 3D data nowadays still is a challenging task. Many barriers are caused by different perspectives of organisations, technical issues during conversions of data and a lack of guidelines. For this research, a case study is done in The Netherlands, one of the countries that experimented a lot with topographical data already. The Dutch Cadastre, Kadaster, has been working on a country wide covering 3D topographical basemap. However, also some of the Dutch source holders of large-scale topography have taken steps towards the development of 3D city models. The question that now arises, and also the main research question of this thesis, is: “How can a variety of 3D city models be integrated in a country wide covering 3D basemap based on large-scale topography?”. In order to achieve an answer to this question, the methodology has been split up into three components. The first component contained a literature study on 3D city models. The second component of the methodology included the interaction with stakeholders, such as Kadaster and various source holders of the Dutch topographical data. This interaction is performed by means of interviews and surveys. The third component contained the technical part, in which different 3D test data is collected and compared. The test data is for almost all stakeholders provided in CityGML, which is an international 3D standard used for 3D models. Various differences in the 3D data of the stakeholders are found. These differences can be found in the contents, the source data, the process and the management. Based on these test data and their differences, a workflow is developed in order to integrate the data. This workflow uses open source tools as cjio and citygml4j for the manipulation, integration and conversion of the data. This resulted in an integrated 3D model, containing both the data from Kadaster as well as the data from the source holders. Results have shown that the differences between the test data, semantically as well as geometrically, led to gaps and height differences in the final integrated model. This study has proven that different 3D city models can be integrated in a country wide covering model, which can be converted to various formats (CityGML, CityJSON and OBJ). A workflow is developed that integrates the test data of 5 Dutch source holders with the data from Kadaster. A few challenges during the conversion and integration of data had to be overcome. These challenges were either caused by errors in the code of the test data or by bugs and errors in the software tools. In the end, two proposals were given for further organisational developments towards a national 3D basemap based on large-scale topographical data. These proposals were based on the results of the literature study, the interviews with stakeholders and the data comparison and integration. In option 1, a national 3D basemap, developed and managed by Kadaster, is proposed. In option 2, a new basis registration, the 3D BGT, is proposed. In this situation Kadaster will provide a 3D basemap once and the source holders will collect and parse the mutations in 3D.

Noise nuisance caused by outdoor music events has become a heavily discussed
issue in metropolitan environments such as Amsterdam. The main objective of this research is to lay the foundation for a map that effectively disseminate noise information and facilitates well-informed decision making concerning the impact and assessment of outdoor music events. A noise map disseminates noise level information and is a common tool for environmental noise caused by roads or industries. The research question is: can a noise map be a valid tool for predicting noise nuisance from outdoor music events in the built environment?
This master thesis is an exploration whether subjective opinions that are the result of a realworld event can be predicted by spatial analysis. Sound is a phenomenon with a spatial attribute and the knowledge of sound propagation is applied frequently in many different industries. In practice noise level prediction is an important instrument to decide by authorities whether an event can take place or not, therefore a study to better understand the relationship between complaints and noise level prediction is valuable.
Noise measurements have been made at several events in the surrounding area of Amsterdam. The noise measurements have been used to investigate the accuracy of the current noise level prediction methodology. Spatial data and noise measurements were gathered from actual noise nuisance complaints to gain insight into the relation between the noise levels and nuisance complaints. Finally recommendations are made to help the municipality and event-producers better assess the possible impact of an outdoor music event.