The Exponential growth of Earth Observation (EO) data volumes in the past decade has made downloading and processing EO data locally impractical. In response, the European public space sector launched initiatives to provide private cloud infrastructure, like the European Weather Cloud (EWC), allowing users to provision computing resources close to the data.

Leveraging these new possibilities introduced by cloud services and machine learning, the hydro-meteorological community has initiated projects to identify features from remote sensing data, including satellite imagery, to enhance early weather warnings and climate science. EUMETSAT and its Member States are now developing a collaborative environment within EWC for manual annotation, model development, and analyses to provide reliable feature identification from EO data.

Join us in our session to learn more about our solution, involving an environment for data preparation, community annotation tools, and a features database.