The MOOC-EOODS project aims to support the joint development of two core skills needed to conduct impactful Earth Observation research (including in the context of the Science Hub): EO satellite data characteristics and data science in the cloud. The course assumes participants to have at least a basic level of computer science knowledge, and familiarity with common programming languages and scripting (e.g., python, R). The course shall provide an introduction to satellite remote sensing, it shall review the most common EO data types and formats and various signal and image processing approaches, focusing on the applicability of the methods to the specific classes of signals. Through practical examples and hands-on exercises executed on European Platforms (such as, e.g., OpenEO, Euro Data Cube, ESA Virtual Laboratories, Earth System Data Lab, etc.), participants will gain a better understanding of:
The various EO sensors and ESA missions and what they measure;
What differentiates Earth Observation data from multimedia;
What types and classes of signals are commonly found in remote sensing applications;
What are the statistical properties of these classes of signals;
Signal Processing and Machine Learning methods for Earth Observation;
How to programmatically access ESA and other open data in a platform environment;
Designing, developing and executing algorithms on EO cloud platforms;
Scalable EO and geospatial analytics in the cloud.
Specifically, MOOC-EOODS shall foster the use of Open Data and in particular EO data from the Copernicus Sentinels, higher level EO products (e.g., from ESA CCI, Earth System Data Lab) and other Open Geospatial data sources (e.g., Open Street Map), providing the basis for dealing with a wide variety of data types and formats and supporting diverse scientific and societal applications.
For more information please visit esa-star.
