AI4GNSSR: Artificial intelligence for GNSS reflectometry
Artificial Intelligence (AI) has rapidly transformed various fields, including natural language processing, computer vision, and data analysis. Recent developments, such as OpenAI's ChatGPT, have demonstrated AI's capability to understand and generate human-like text, revolutionizing customer service, content creation, and more. These advancements highlight AI's potential to enhance data processing and interpretation across diverse domains.
The AI4GNSSR project (Artificial Intelligence for GNSS Reflectometry: Novel Remote Sensing of Ocean and Atmosphere) is funded by Helmholtz AI and is a collaborative project with the Remote Sensing Technology Institute of German Aerospace Center DLR. AI4GNSSR is an innovative project that leverages AI to enhance the processing of spaceborne GNSS Reflectometry (GNSS-R) measurements for monitoring the ocean and atmosphere. GNSS-R involves the use of signals from the Global Navigation Satellite System (GNSS) after they reflect off the Earth's surface. These reflections create vast datasets with unique potentials for characterizing Earth systems using AI.
Key deliverables expected from AI4GNSSR include:
- Novel GNSS-R geophysical data products.
- Enhanced quality of existing data products, such as wind speed data, particularly in extreme weather conditions.
- Empirical refinement of existing theoretical physical models in the young field of GNSS-R remote sensing.
This project promises to bring groundbreaking advancements in remote sensing, providing more accurate and reliable data for monitoring Earth's oceans and atmosphere. By incorporating state-of-the-art AI techniques, AI4GNSSR aims to push the boundaries of what is possible in GNSS-R, much like AI has done in other fields.
References
Asgarimehr, M., Arnold, C., Weigel, T., Ruf, C., & Wickert, J. (2022). GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet. Remote Sensing of Environment, 269, 112801.
Arabi, S., Asgarimehr, M., Kada, M., & Wickert, J. (2023). Hybrid CNN-LSTM Deep Learning for Track-Wise GNSS-R Ocean Wind Speed Retrieval. Remote Sensing, 15(17), 4169.
Zhao, D., Heidler, K., Asgarimehr, M., Arnold, C., Xiao, T., Wickert, J., ... & Mou, L. (2023). DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation. Remote Sensing of Environment, 294, 113629.
