Machine Learning-Based Exploitation of Crowdsourced GNSS Data for Atmospheric Studies

The Global Navigation Satellite System (GNSS) is a well-recognized tool to probe the Earth’s atmosphere. This contribution highlights how GNSS data collected from smartphones of voluntary contributors can be used to determine parameters of the troposphere and ionosphere. In this regard, the application of machine learning (ML) to characterize the quality of the crowd-sourced data and model atmospheric parameters is discussed. We demonstrate that in certain cases, GNSS data from smartphones can reach a precision that would allow such data to densify observations from existing geodetic infrastructures.