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Earth Observation scientists are confronted with unprecedent data volumes that are constantly growing as the number of satellite missions increases, as well as spatial and temporal resolution do. Traditional working modes, such as downloading satellite data and processing it on a local computer, do not apply anymore and EO science is moving quickly towards cloud technologies and open science practices. Even though these new technologies and practices are evolving quickly and are becoming community standard, there is not much educational material available to educate the next generation of EO scientists. The Massive Open Online Course Cubes Clouds - Cloud Native Open Data Sciences for Earth Observation teaches the concepts of data cubes, cloud platforms and open science in the context of earth observation. It targets Earth Science students and researchers who want to increase their technical capabilities onto the newest standards in EO computing, as well as Data Scientists who want to dive into the world of EO and apply their technical background to a new field. Before starting, prerequisites are a general knowledge of EO and python programming. The course explains the concepts of data cubes, EO cloud platforms and open science by applying them to a typical EO workflow from data discovery, data processing up to sharing the results in an open and FAIR (Findable, Accessible, Interoperable, Reusable) way. An engaging mixture of videos, animated content, lectures, hands-on exercises and quizzes transmits the content. After finishing the participant will understand the theoretical concepts of cloud native EO processing and have gained practical experience by conducting an end-to-end EO workflow. The participant will be capable of independently using cloud platforms to approach EO related research questions and be confident in how to share research by adhering to the concepts of open science.The hands on exercises are carried out on the EO cloud platform Copernicus Data Space Ecosystem and are leveraging the Copernicus Data available through the STAC Catalogue and the cloud processing API openEO. In the final exercise the participants collaborate on a community mapping project adhering to open science and FAIR standards. A snow cover map is jointly created where every participants maps a small area of the alps and submits it to a STAC catalogue and web viewer. Ultimately, the map grows in space and time and every participant has contributed proving they are capable of EO cloud computing and open science practices.The talk will guide through the topics covered in the MOOC and show how they are presented in the EOCollege e-learning platform, the links to the exercises carried out on CDSE will be explored and the open science aspect will be shown in the community mapping project and the invitation to collaborate on the courses completely open GitHub repository.
Zellner et al. (Mon,) studied this question.