Institution: International Institute for Applied Systems Analysis / ETH Zurich
Project lead: Ian McCallum / Benedikt Soja
Schlossplatz 1, 2361 Laxenburg 2361 / Robert-​Gnehm-Weg 15, 8093 Zürich, Schweiz
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. / This email address is being protected from spambots. You need JavaScript enabled to view it.


The CAMALIOT (Application of Machine Learning Technology for GNSS IoT Data Fusion) project

The CAMALIOT project integrates data from the Internet of Things (IoT), including smartphones, and traditional Global Navigation Satellite System (GNSS) data sources to leverage Big Data, Data Fusion and Machine Learning technologies to to demonstrate how these data can be used in different scientific applications. 

The overall objectives of the CAMALIOT project are to: 

  • Assess the potential of different sources of raw GNSS data for ingestion to machine learning algorithms; 
  • Develop a crowdsourcing Android smartphone app for the acquisition of raw, crowdsourced GNSS data;
  • Implement a Big Data repository for subsequent data fusion (i.e., data from smartphones and other IoT providers) with more traditional GNSS data sources; and
  • Define and implement two use cases related to improving the prediction of extreme weather events in the atmosphere and in space. 

The project is currently in the crowdsourcing phase and a campaign was launched on March 17, 2022. Taking advantage of dual frequency chipsets now available in some Android mobile phones, the CAMALIOT Android app logs data from all available satellites. The purpose of the crowdsourcing campaign is to collect as much raw GNSS data as possible from as many locations around the world using crowdsourcing. The data will then be ingested into machine learning algorithms for determination of tropospheric parameters that support weather forecasts on Earth and for the monitoring of space weather, important for satellite operations and communication. More information about the results will appear on www.camaliot.org as the project progresses.

The project has three main partners: (i) the European Space Agency, who both fund and provide scientific support; (ii) ETH Zurich, who lead the project and are developing the machine learning models for weather prediction; and (iii) the International Institute for Applied Systems Analysis (IIASA), who have developed the CAMALIOT crowdsourcing app and lead the data collection campaign as outlined on the project's website.


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  • weather


Read 87 times| Last modified on Tuesday, 17 May 2022 10:16