In the past, such an analysis was nearly impossible for users with commercial devices (e.g., smartphones), because users can only access the GPS location indicators as distilled through sophisticated filtering processes. Fortunately, new devices [17], supported since Android 7, open up the access to raw GNSS measurements. (global navigation satellite system (GNSS) is a generic term referring to satellite navigation systems such as GPS and Galileo; however, here, raw GNSS measurements denote the standard data format shared by all such systems, though we actually obtain (only) GPS-GNSS measurements). Although the GPS location indicators are derived from these GNSS measurements, when substantial information is lost, it may have been caused by the significant dimension reduction resulting from conventional signal processing pipelines. Therefore, it is reasonable to believe that effectively mining a large volume of these raw measurements could end up with a much more holistic profiling in urban environments than with binary IO-detection. With this initial attempt in profiling, we can not only characterize a location semantically (which an erroneous location indicator likely fails to achieve), but also gain a better understanding (or even correction) of the large number of location errors observed in urban areas.  
To this end, we thoroughly conduct GNSS data collection in a major metropolitan city, targeting representative scenarios such as sheltered walkways, urban canyons, buildings with an open ground level, and so on. Our aim is to process these data using effective machine learning tools, so as to distill latent representations out of them. These representations, on one hand, are used to construct semantic profiles for characterizing various urban location contexts, and on the other, they can be used to provide a better accuracy indicator than, for example, the \"blue disk\" offered by Google Maps. One of the major challenges in mining GNSS data is its high dimensionality: each record of GPS-GNSS measurements includes up to 32 satellites, with several tens of fields for each of them. This is significantly different from the time-series or image inputs to machine learning algorithms. Another major challenge lies in the inherent misalignment in feature space over time: the same location can be served by very different satellites at various points in time. This is also the reason that we consider profiling locations _contexts_ (rather than merely locations), as other factors beyond pure locations should be taken into consideration. In order to make sense out of these data, we make the following main contributions:  
* We, for the first time, propose to profile location context via mining raw GNSS measurements.
* We innovate in the feature engineering pipeline to handle both high-dimensionality and misalignment.
* We apply a deep autoencoder to distill compressed representations from the GNSS data sets.
* We develop two preliminary applications of our deep profiling; they deliver semantic representations and better location error estimations, respectively.
* We perform extensive data collection and experimentation to validate our proposals.  
Note that we are not aiming to deliver yet another optimized GPS [2; 12; 18], although a future research direction could be combining conventional signal processing techniques with machine learning schemes. Instead, our main target is to demonstrate to the community that we have left out a substantial amount of information hidden in GNSS data by deriving only location indicators. Furthermore, we also deliver some preliminary outcomes of mining such latent information, hoping to evoke more mature applications.