%0 Conference Paper %1 dlr142309 %A Fichtner, Florian Willy %A Mandery, Nico %A Schwinger, Maximilian %A Eberle, Jonas %A Nolde, Michael %A Riedlinger, Torsten %B Big Data from Space 2021 %D 2021 %K cloud descartes earth_observation hpc processing t_interdisciplinary t_techreport myown %P 77--80 %T Scalable Processing Of Copernicus Sentinel satellite Images Using Argo Workflows %U https://elib.dlr.de/142309/ %X With the goal of using a pool of resources dynamically for different applications we migrated scientific Earth Observation processors from dedicated Virtual Machines into a centralized cloud. Processing in this environment works using off-the-shelf components like Kubernetes, Docker and Argo Workflows. We were able to benefit from sharing data, tools and infrastructure in between processors. To showcase the new workflow or pipeline by means of an example we use our burnt area monitoring processor based on Sentinel-3 satellite data. A new architecture based on existing components was established, which we plan to extend it in the future.

@inproceedings{dlr142309, abstract = {With the goal of using a pool of resources dynamically for different applications we migrated scientific Earth Observation processors from dedicated Virtual Machines into a centralized cloud. Processing in this environment works using off-the-shelf components like Kubernetes, Docker and Argo Workflows. We were able to benefit from sharing data, tools and infrastructure in between processors. To showcase the new workflow or pipeline by means of an example we use our burnt area monitoring processor based on Sentinel-3 satellite data. A new architecture based on existing components was established, which we plan to extend it in the future.}, added-at = {2023-12-20T15:54:59.000+0100}, author = {Fichtner, Florian Willy and Mandery, Nico and Schwinger, Maximilian and Eberle, Jonas and Nolde, Michael and Riedlinger, Torsten}, biburl = {https://www.bibsonomy.org/bibtex/211235563188a94f0e7aabf0045e3721c/max.schwinger}, booktitle = {Big Data from Space 2021}, interhash = {ed4e540f491d39afc30881f9af636ba4}, intrahash = {11235563188a94f0e7aabf0045e3721c}, keywords = {cloud descartes earth_observation hpc processing t_interdisciplinary t_techreport myown}, month = may, pages = {77--80}, timestamp = {2023-12-20T15:54:59.000+0100}, title = {Scalable Processing Of Copernicus Sentinel satellite Images Using Argo Workflows}, url = {https://elib.dlr.de/142309/}, year = 2021 }