The study of public space activity has been one of the main foci in the debate on urban space transformations for the past decades, with many researchers adding to the debate through theoretical work, as well as empirical/quantitative evidence, drawing from their direct observations of public space activity. This paper attempts to enhance this approach of urban research and public space observation, by investigating the application of remote sensing techniques in public space analysis. More specifically, it attempts to capture public space activity using publicly available digital traces, such as environmental and temporal data, as well as social media data streams. By applying bivariate and multivariate analysis techniques to these datasets, it illustrates the possibility of capturing current activity in public spaces, with some degree of confidence. Furthermore, given the ubiquitous and real-time nature of these datasets, it also becomes possible to provide continuous estimates as well as short-term predictions on current and near-future public space use. Finally, it outlines the capabilities of this approach, to be used in complementary fashion to direct observation methods mentioned above, in building high resolution models and simulations of public space activity.