Abstract
In a landscape of ever-growing access to free data, the problem of how to process all this data remains a challenge. With this article, we show how we tackle these challenges within the ESA SEOM project SInCohMap, where a critical task is to host a round robin to test different classification approaches for land cover mapping utilizing information derived from complex SAR data, in particular the evolution of multi-temporal coherence from the Copernicus Sentinel-1 satellites. Access to data and processing facilities is provided by the Eurac Research Sentinel Alpine Observatory on their computing infrastructure based on free open source technology, featuring cloud computing on OpenNebula and Kubernetes, multi-dimensional data arrays on Rasdaman and web-based python development on Jupyter.