Lingke Wang, M.sc.

Curriculum Vitae

since Oct 2022	Ph.D. student at the Geodetic Institut at KIT, Germany

Sep. 2019 - Oct. 2021	M.Sc. in Geomatic Engineering at Universität Stuttgart, Germany

Sep. 2015 - Jun. 2019	B.Sc. in Geodesy and Geoinformatics at Wuhan University, China

Research Interests

Lingke Wang’s research combines artificial intelligence (AI) with GNSS data processing and atmospheric remote sensing. Machine learning and deep learning methods are developed to model measurement errors, estimate tropospheric delay and integrated water vapor (IWV), and integrate observations from ground-based sensors, satellites and numerical weather models. Particular attention is given to spatial and temporal generalization, uncertainty and the interpretation of validation results.

AI for GNSS error modeling
Tropospheric delay and water vapor estimation
Multi-source water vapor fusion and bias correction

Figure. Data sources, methods and applications in AI-supported GNSS processing and atmospheric water vapor sensing. PCA provides residual decomposition before machine learning in the multipath framework.

Publications

Wang, L.; Wang, D.; Awange, J.; Kutterer, H. (2025). High-Resolution Integrated Water Vapor Estimation Using the Gaussian Mixed Long Short-Term Memory Network: A Satellite-Based Inter-Comparison and Data-Fusion. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–20. doi:10.1109/TGRS.2025.3638337
Wang, D.; Wang, L.; Kutterer, H. (2025). An Advanced Tropospheric Delay Model Based on Gaussian Mixed Long Short-Term Memory Network. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–14. doi:10.1109/TGRS.2025.3549124
Wang, L.; Kutterer, H. (2025). An advanced regional integrated water vapor estimation model utilizing least squares support vector machine for the upper Rhine graben region. GPS Solutions, 29 (1), Art.-Nr.: 59. doi:10.1007/s10291-024-01796-3