Earth observation is generating huge amounts of data that have to be processed automatically, fast, and in near real time. Since the launch of the first Earth observation satellites we are seeing a steady increase in the number of operational satellites. The satellites have better spatial resolution (smaller pixels), better spectral resolution (more bands) and better temporal resolution (images are acquired more often). In the past we were dealing with data volumes in the order of tens to hundreds of GB, now we are moving to TB and even larger numbers.

Within the Earth observation initiative Copernicus European Union and European Space Agency are developing a new family of missions called Sentinels. Each Sentinel mission is based on a constellation of two satellites to fulfill revisit and coverage requirements, providing robust datasets for Copernicus Services. These missions carry a range of technologies, such as radar and multi-spectral imaging instruments for land, ocean and atmospheric monitoring. The Sentinels will generate approximately 1400 TB for Europe, i.e. 5 TB of data per year in Slovenia or 190 TB for Balkans.

Traditional image processing started in the ground station from where the satellite data was transferred remote sensing experts for additional processing. These prepared the value-added data to users who use the images in their specific applications. This results in duplication of data and expertise. Every expert organization has to replicate the data and develop its own data processing chain (input, storage, geometric and radiometric correction, etc.) that is usually not automatic. At the end the results have to provided to the end users, that need information and do not care about the while processing. The results can be printed or online maps, reports etc.

Recent development in cloud computing promises to overcome most of the limitations present in the traditional approach. Cloud computing in support to Earth observation is still only in its infancy. Nonetheless, it is a quickly growing field that has already led to first remarkable scientific results. Space-SI is testing an automatic image processing chain that performs all processing steps from sensor-corrected (Level 1) optical satellite images to web-delivered mapready products. It is a near-real-time processing workflow that operates fully automatic with no operator's intervention required. It performs several steps, starting with automatic geometric and radiometric pre-processing, followed by a variety of automatic interpretations and analyses. To optimize the use of spatial data acquired with our satellite system or other data sources (partner satellites, governmental data, etc.) we have built a distribution system based on web and mobile applications. The system is based on Geopedia, a cloud-based web GIS viewing and editing solution, developed by Sinergise.