MACHINE LEARNING-BASED EXPLOITATION OF CROWDSOURCED GNSS DATA FOR ATMOSPHERIC STUDIES

被引：2

作者：

Soja, Benedikt ^{[1
]}

Klopotek, Grzegorz ^{[1
]}

Pan, Yuanxin ^{[1
]}

Crocetti, Laura ^{[1
]}

Mao, Shuyin ^{[1
]}

Awadaljeed, Mudathir ^{[1
]}

Rothacher, Markus ^{[1
]}

See, Linda ^{[2
]}

Sturn, Tobias ^{[2
]}

Weinacker, Rudi ^{[2
]}

McCallum, Ian ^{[2
]}

Navarro, Vicente ^{[3
]}

机构：

[1] Swiss Fed Inst Technol, Inst Geodesy & Photogrammetry, Zurich, Switzerland

[2] Int Inst Appl Syst Anal IIASA, Laxenburg, Austria

[3] European Space Agcy, European Space Astron Ctr, Villanueva De La Canada, Spain

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

GNSS; crowdsourcing; machine learning; atmosphere;

D O I：

10.1109/IGARSS52108.2023.10283441

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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 crowdsourced 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.

引用

页码：1170 / 1173

页数：4

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