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.

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.