Mobile device hackers often target ambient sensing, human activity identification, and interior floor mapping. In addition to overt signals like microphones and cameras, covert channels like WiFi, Bluetooth, and augmented GPS signal strengths have been employed to gather this information. Until date, passive, receive-only satellite GPS sensing relied solely on signal strength and location information. This paper demonstrates that semi-processed GPS data (39 features) accessible to apps since Android 7 with precise location permissions can be used as a highly accurate leaky channel for sensing ambient, recognising human activity, and mapping indoor spaces (99%+ accuracy). This report describes a longitudinal research that used semi-processed GPS readings from mobile devices throughout a 40,000 sq. km region for a year. Data was acquired from aeroplanes, cruise ships, and high-altitude places. To retain crucial information, we analyse all satellite GPS signals and select the best characteristics using cross-correlation analysis. Our work, AndroCon, combines lin-ear discriminant analysis, unscented Kalman filtering, gradient boosting, and random forest learning to provide an accurate ambient and human activity sensor. At AndroCon, basic ML algorithms are used for discreet and somewhat explainable outcomes. We can readily recognise challenging situations, such as being in a subway, when someone is waving a hand in front of a mobile device, in front of a stairway, or with others present (not always carrying phones). This is the most extensive study on satellite GPS-based sensing as of yet.