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

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

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

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

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

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

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

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

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.

Wind has a profound impact on the meteorological and environmental conditions in cities. And so, by understanding wind flow behaviour within the urban environment, we can use the increasingly available open data to contribute to the design of healthy cities. This Master’s thesis presents a methodology to compute urban morphological parameters and its effect on potential wind velocity. The proposed method may serve as an complementary method to Computational Fluid Dynamics (CFD) simulation or scaled wind tunnel tests. The research question in this thesis is: Can we use urban morphology to automatically calculate potential increase in wind velocity? To answer this, first an introduction to wind flows in the urban environment
is presented. Then, several methodologies are presented to compute the urban morphological parameters, such as the Urban Canyon, wind- or leeward facade, Angle of Attack and terrain roughness length. The method relies on the use of a Voronoidiagram, with cells describing the morphology. After, the urban morphological parameters are related to potential wind velocity through a scoring method. Using two meteorological stations inside the area of interest, the mean wind velocity is compared to the scores. The result show that both stations show a higher mean wind velocity for higher scores. However, more research is necessary to validate this outcome and a recommendation is given to compare the result of this thesis to a CFD simulation.

The possibilities of 3D city models for the analysis of the built environment are increasingly explored, and there is a continuous development in improvements on their inner workings. They are also used more often on the web, for example for visualisation but it is also possible to query, analyse, and edit data in this way. In this case, the network becomes a potential new bottleneck in time performance. Especially when a 3D city model contains a lot of attributes, there is a rapid increase in file size when the area of study is expanded.
This presents challenges in efficiency and in this thesis I focus on the improvement of the inner workings of 3D city models to attempt to relieve this problem, in specific for spreading and using them more efficiently on the web.

By investigating and testing different compression techniques on 3D city models stored in the CityJSON format, I have attempted to relieve this problem. CityJSON is already more compact than CityGML and these techniques decrease the file sizes of datasets even further, allowing for faster transmission over a network. But on the other hand, additional steps are needed to process compressed files. The goal of using a compression technique is to result in a net speed gain, meaning that the time that is saved on download time should be larger than the additional time that it costs to process the file before transmission (compressing the data on the server) and after receival (decompressing the data in the client).

There are compression techniques for both general and specific purposes, and I have used combinations of them. Amongst these are Draco compression, zlib, CBOR, and a self-created technique. Specific ones are used for the main characteristics that CityJSON datasets have, which are the JSON structure, feature attributes, and feature geometries. To assess the impact that different combinations of compression techniques have, I take uncompressed CityJSON performance as the baseline and have come up with different performance indicators that include several use cases such as visualisation, querying, analysis, and editing.

I have benchmarked all combinations of compression techniques on the use cases of these performance indicators. For this I have created two types of server implementations: one with which datasets are compressed beforehand and are processed in the client based on the request made by the user, and one where the data is processed first and only then compressed and transmitted to the server. In the results, you can see the best-performing compression type per use case.

The benchmarking is performed on a variety of datasets that are split into four categories: larger datasets, larger datasets without attributes, smaller datasets, and smaller datasets without attributes. This ultimately makes for use cases that are very specific and choosing suitable compression types requires finding out which ones perform relatively well in most cases, and are not difficult to implement in order to keep CityJSON a simple file format. It turns out that Draco compression can give good results in specifc situations, but in general is not good to use. Not only regarding performance, but also from a developer point of view. CBOR, zlib, and a combination of these two are easy to use and generally affect the performance of CityJSON on the web in a good way.

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.

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.