Benedikt Soja

Orcid: 0000-0002-7010-2147

According to our database1, Benedikt Soja authored at least 13 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Uncertainties of Interpolating Satellite-Specific Slant Ionospheric Delays and Impacts on PPP-RTK.
IEEE Trans. Aerosp. Electron. Syst., February, 2024

Global Ionospheric Modeling Using Multi-GNSS: A Machine Learning Approach.
Proceedings of the IGARSS 2024, 2024

Modelling the Troposphere with Global Navigation Satellite Systems, Meteorological Data and Machine Learning.
Proceedings of the IGARSS 2024, 2024

2023
Modeling the Differences between Ultra-Rapid and Final Orbit Products of GPS Satellites Using Machine-Learning Approaches.
Remote. Sens., December, 2023

Analyzing the Ionospheric Irregularities Caused by the September 2017 Geomagnetic Storm Using Ground-Based GNSS, Swarm, and FORMOSAT-3/COSMIC Data near the Equatorial Ionization Anomaly in East Africa.
Remote. Sens., December, 2023

Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project.
Int. J. Digit. Earth, December, 2023

Machine Learning-Based Exploitation of Crowdsourced GNSS Data for Atmospheric Studies.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

2022
Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach.
Remote. Sens., 2022

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting.
Remote. Sens., 2022

Data Driven Approaches for the Prediction of Earth's Effective Angular Momentum Functions.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning.
Remote. Sens., 2021

Small Geodetic Datasets and Deep Networks: Attention-Based Residual LSTM Autoencoder Stacking for Geodetic Time Series.
Proceedings of the Machine Learning, Optimization, and Data Science, 2021

Modified Deep Transformers for GNSS Time Series Prediction.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021


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