GSSC Now - ESA's Thematic Exploitation Platform for Navigation Science Data

Nowadays, the sheer amount of data collected from space-borne and ground-based sensors, is changing past approaches towards data processing and storage. In the Information Technology domain, the rapid growth of data generation rates, expected to produce 175 zettabytes worldwide by 2025, is changing approaches to data processing and storage dramatically. This landscape has led to a new golden age of Machine learning (ML), able to extract knowledge and discover patterns between input and output variables given the sheer volume of available training data.

In space, over 120 satellites from four Global Navigation Satellite Systems (GNSS), including Galileo, will provide, already this decade, continuous, worlwide signals in several frequencies. On ground, the professional market represented by thousands of permanent GNSS stations has been complemented by billions of mass-market receivers integrated in smartphones and Internet-of-Things (IoT) devices.

Along their travel down to Earth through the atmosphere, multiple sources alter the precisely modulated GNSS signals. As they pass through irregular plasma patches in the ionosphere, GNSS signals undergo delay and fading, formally known as 'scintillation'. Further down, they are modified by the amount of water vapor in the troposphere. These alterations, recorded by GNSS receivers as digital footprints in massive streams of data, represent a valuable resource for science, increasingly employed to study Earth’s atmosphere, oceans, and surface environments.

In order to realize the scientific potential of GNSS data, at the European Space Astronomy Centre (ESAC) near Madrid, the GNSS Science Support Centre (GSSC) led by ESA’s Navigation Science Office, hosts ESA’s data archive for scientific exploitation of GNSS data.

Analysis of Global Navigation Satellite Systems (GNSS) data has traditionally pivoted around the idea of datasets search and download from multiple repositories that act as data-hubs for different types of GNSS resources generated worldwide. In this work we introduce an innovative GNSS Thematic Exploitation Platform, GSSC Now, which expands a GNSS-centric data lake with novel capabilities for discovery and high-performance-computing.

We explain how this platform performs GNSS data fusion from multiple data sources, enabling the deployment of Machine Learning (ML) processors to unleash synergies across science domains.

Finally, through the presentation of several GNSS science use cases, we discuss the implementation of GSSC Now’s cyber-infrastructure, current status, and future plans to accelerate the development of innovative applications and citizen-science.