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

Soja, B., Kłopotek, G., Pan, Y., Crocetti, L., Mao, S., Awadaljeed, M., Rothacher, M., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, et al. (2023). Machine Learning-Based Exploitation of Crowdsourced GNSS Data for Atmospheric Studies. In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. pp. 1170-1173 Pasadena: IEEE. 10.1109/IGARSS52108.2023.10283441.

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Abstract

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.

Item Type:	Book Section
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User:	Luke Kirwan
Date Deposited:	24 Oct 2023 07:29
Last Modified:	24 Oct 2023 07:29
URI:	https://pure.iiasa.ac.at/19147

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