How to structure EO data for ML workflows
The availability of open Earth observation (EO) data represents an unprecedented resource for many EO applications. The value hidden within an open access satellite imagery can not only be revealed by looking at spatial context but also by taking into account the temporal evolution of a pixel or an area within an image. We found available data structures not best suited for an automatic extraction of complex patterns in such spatio-temporal data. In this talk we will present lessons learned dealing with optical imagery from Sentinel-2 satellite with a five-day global revisit time. The value extraction pipelines relying on other external machine learning and deep learning frameworks are streamlined with the
eo-learn library in which an
EOPatch plays a central role as a data container.