Workshop motivation and description
The use of Machine Learning/Deep Learning (ML/DL) techniques is becoming widespread in a large and ever-growing number of application areas in Earth System Observation and Prediction (ESOP). Additionally, the scale, complexity and sophistication of the ML/DL technologies applied in ESOP has also increased considerably over the last few years, reflecting the growing uptake of ML/DL ideas in the ESOP communities and benefiting from increased interest of ML/DL domain scientists and of large commercial players. As a result, ML/DL tools are increasingly integrated in ESOP applications and in some areas they show promise of substituting traditional methodologies.
The third edition of the ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction aimed to provide an up-to-date snapshot of the state of the art in this rapidly evolving field and to facilitate discussion among scientists and practitioners about the current opportunities and challenges in the use of ML/DL technologies for ESOP.
Thematic areas that were covered in this workshop include:
1. Machine Learning for Earth Observations
2. Hybrid Machine Learning - Data Assimilation
3. Machine Learning for Model emulation and Model discovery
4. Machine Learning for user-oriented Earth Science applications
5. Machine learning at the edge and high-performance computing