A data-driven approach to add openings to 3D BAG building models

The reconstruction of 3D city models has garnered significant interest in recent years. However, the majority of existing reconstruction methods primarily focus on LOD2 models, while LOD3 model reconstruction often relies on manual labor, and the primary data sources are street view images. This research aims to advance this field by...

Information extraction and geolocalization of historical aerial imagery

Antarctica plays a crucial role in global climate change research, but current models only extend back to the satellite era from the 1990s to the present, leaving significant gaps in our understanding of the continent’s past and covering only limited locations. Historical aerial surveys, which may extend back to the 1930s and cover more...

Automated rooftop solar panel detection through Convolutional Neural Networks

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient...

Automated Semantic Segmentation of Aerial Imagery using Synthetic Data

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...

Seasoning: Synthesizing aerial imagery for the different seasons of the year

Nowadays technology has advanced so far that there are a lot of ways of capturing aerial imagery, causing this imagery to be widely available for a lot of areas in the world. Unfortunately, there are still types of aerial imagery that are not as widely available, such as images depicting the different seasons of the year. This thesis presents a...

Semantic Segmentation of RGB-Z Aerial Imagery Using Convolutional Neural Networks

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...

Large-scale efficient extraction of 3D roof segments from aerial stereo imagery

3D reconstruction of the built environment is a widely studied Geomatics topic. Resulting city models are used for a great variety of purposes. The reconstruction of roofs remains challenging. These roofs are not only building blocks for the city models but are of direct interest as well. The company READAAR uses roof segments for the detection...