Autonomous mobile robots show promising opportunities as concrete use cases of location-based services. Such robots are able to perform various tasks in buildings using a wide array of sensors to perceive their surroundings. A connected area of research which forms the basis for a deeper understanding of these perceptions is the numerical representation of visual perception of space. Different structures in buildings like rooms, hallways and doorways form different, corresponding patterns in these representations. Thanks to recent advances in the field of deep learning with neural networks, it now seems possible to explore the idea of automatically learning these recurring structures using machine learning techniques. Combining these topics will enable the creation of new and better location-based services which have a deep awareness of their surroundings. This paper presents a framework to create a data set containing 2D isovist measures calculated along geospatial trajectories that traverse a 3D simulation environment. Furthermore, we show that these isovist measures do reflect the recurring structures found in buildings and the recurring patterns are encoded in a way that unsupervised machine learning is able to identify meaningful structures like rooms, hallways and doorways. These labeled data sets can further be used for neural network based supervised learning. The models generated this way do generalize and are able to identify structures in different environments.