Yuxin QIN

Research

I am a geodesist interested in the theory and methods of surveying-data processing. My research combines mathematical modeling, statistical estimation, and machine-learning approaches to improve the accuracy, robustness, and efficiency of geodetic measurements and Earth-observation products.

My current interests include geodetic coordinate transformation, robust estimation, total least-squares methods, atmospheric water-vapor retrieval, ionospheric modeling, and the application of machine learning—including XGBoost, artificial neural networks, and LSTM models—to remote-sensing and geodetic data.

Background

I received an M.Sc. in Geodesy and Survey Engineering from Wuhan University, China, in 2024. I previously obtained a B.Sc. in Geodesy and Geomatics Engineering from Wuhan University in 2021.

Research interests

• Theory and methods of surveying-data processing
• Geodetic estimation and coordinate transformations
• Total least-squares theory
• Robust estimation and Huber-function optimization
• GNSS, ionospheric, and atmospheric modeling
• Precipitable water-vapor retrieval
• Remote sensing and MODIS data products
• Machine learning for geodesy and Earth observation

Representative publications

• Qin, Y., Wang, Y., Zhang, B.*, Fang, X., Yao, Y., and Ma, X. (2023), A novel model integrating spherical cap harmonic analysis with the XGBoost algorithm to improve MODIS NIR PWV, IEEE Transactions on Geoscience and Remote Sensing, 61, 5624112. https://doi.org/10.1109/TGRS.2023.3326659
• Qin, Y., and Fang, X. (2023), On the exact and efficient solution of the Huber function for measurement applications, Measurement, 207, 112416. https://doi.org/10.1016/j.measurement.2022.112416
• Qin, Y., Fang, X., Zeng, W., and Wang, B. (2020), General total least squares theory for geodetic coordinate transformations, Applied Sciences, 10(7), 2598. https://doi.org/10.3390/app10072598
• Wang, Y., Qin, Y., Kong, J., Yao, Y., and Gao, X. (2024), Ionospheric refined mapping function construction based on LSTM, IEEE Transactions on Geoscience and Remote Sensing, 62, 4100115. https://doi.org/10.1109/TGRS.2023.3338777
• Hu, Y., Fang, X., Qin, Y., and Akyilmaz, O. (2022), Weighted geometric circle fitting for the Brogar Ring: Parameter-free approach and bias analysis, Measurement, 192, 110832. https://doi.org/10.1016/j.measurement.2022.110832
• Ma, X., Yao, Y., Zhang, B., Qin, Y., Zhang, Q., and Zhu, H. (2022), An improved MODIS NIR PWV retrieval algorithm based on an artificial neural network considering land-cover types, IEEE Transactions on Geoscience and Remote Sensing, 60, 5622412. https://doi.org/10.1109/TGRS.2022.3170078