Preperation of scenarios for the performance optimization of a content-based remote sensing image mining system

Recent development in the design of modern satellite ground segments include systems and tools for automated content analysis allowing users to conduct systematic semantic searches within satellite image data archives. The need for such tools becomes more and more pressing as future space-borne imaging sensors will deliver enormous quantities of data that cannot be studied manually. For instance, typical examples from a European perspective are described in [1] and [2]. Within this framework, the European Space Agency (ESA) has started to fund the Earth Observation Librarian (EOLib) project to set up the next generation of image information mining systems [3]. Here we report on the preparation of scenarios that are needed for training and to verify and optimize the performance of such systems.