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.

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.