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

Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to be able to determine the available roof surface for new solar panel installation, and therefore to perform more accurate solar potential analysis. For this purpose, three different methods are developed and implemented to increase the results’ accuracy, which are geometry-based, unsupervised, and supervised classification. While AHN3 point cloud and 3D BAG Level of Detail (LoD) 2.2 building models are used for the geometry-based classification, the input data of the unsupervised image classification consists of aerial images and BAG footprints. Finally, supervised image classification method makes use of the aerial images as well as the BAG footprints and a dataset of manually labelled solar panel polygons. The results show that the accuracy of individual methods is not sufficient; therefore, the outputs of all three methods are merged together into one pipeline, with the aim of obtaining one final end product. The latter is the 3D BAG LoD2.2 building model in CityJSON format, enhanced with three new attributes per building: the obstacle area on the roof, the available area for installing solar panels, and a Boolean value showing whether the building has existing solar panels or not. Additionally, an enhanced point cloud for future use is generated, with a new attribute per point indicating its distance to the 3D model and therefore its potential for being an obstacle or not. The assessment of the results with the ground truth illustrates that the algorithm gives promising results; however, the scope of the project can be broadened, and improvements can be made to increase the accuracy as well as the efficiency.