In recent years, the tremendous growth of Earth Observation (EO) data, reaching 12 terabytes per day through the Sentinel program alone, has created new challenges for archiving, distributing, and using these ever-growing time series. In response, mundialis has developed forward-looking prototypes for cloud-based infrastructures that enable efficient processing and analysis of large EO time series.
Projects implemented major components of pilot applications focused on sentinel time series. This includes deploying actinia and the openEO GRASS GIS backend on Kubernetes clusters and automatically updating them using Helm charts for Kubernetes. In addition, the methods for time series analysis have been extended both in GRASS GIS and in the openEO GRASS GIS backend.
Most recently, an innovative redesign of actinia took place, in which actinia was divided into a manager and a worker part in order to optimize the work processes. This division was made possible by outsourcing the job queue to a Redis database and supported by newly developed actinia plugins for parallelization.
Another advance was the integration of actinia into a high-performance cluster (HPC) using Charliecloud containers and the implementation of the actinia-stac plugin to create STAC catalogs directly from the data. All these innovations lead to automated installation of actinia via pipelines, including the creation of Charliecloud containers, minimizing potential security risks.