In this paper, we address the problem of how automated situation-awareness can be achieved by learning real-world situations from ubiquitously generated mobility data. Without semantic input about the time and space where situations take place, this turns out to be a fundamental challenging problem. Uncertainties also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for finding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the category, and the actual identity of a space can be discovered from the extracted situation patterns. Doing so, we use different real-world mobility datasets with data about the presence of mobile entities in a variety of spaces. We also evaluate the performance of this technique by showing its robustness against uncertainties.