Earth Observation Data Analysis Library

Imagery from Earth observing (EO) satellites combined with environmental data about climate, topography and soils holds great potential to advance our knowledge about the dynamics of our planet. Still, the handling and analysis of these data sources is cumbersome and presents a high barrier to entry leaving the potential of EO data underexploited. In response to these challenges, we present the open-source Python EO data analysis library (EOdal) that harnesses various EO data sources (Sentinel-1, Sentinel-2, Landsat 1-9, PlanetLabs, gridded environmental data, and many more) in an easy-to-learn framework for reproducible and scalable EO research.

Submitted by Lukas Graf.