Suitability Coverage Engine - SUCE

This project addresses the technology development, benchmarking and prototyping for a suitability coverage engine able to minimise the data transfer and manual manipulation in the production of cloud free coverages.  

In view of forthcoming EO missions, Payload Data Ground Segment (PDGS) infrastructures shall be able to provide easy and powerful data search and data access capabilities, permitting to cope with the very huge amount of data and to give users the possibility to take advantage from dense temporal and large spatial coverages. The proposed Suitability Coverage Engine aims at providing users with the capability to:Perform suitability analysis on archived data sets based on available metadata.Return suitability results (metadata) to analyse potential gaps and allow modifying the suitability criteria.Use the metadata to directly download the "suitable dataset" from the online server or archive. The aim is to derive optimal data set suitable for the mapping task thus avoiding transfer of useless data. As such, the Suitability Coverage Engine shall be, as much as possible flexible, and generic and shall take into account:Standard criteria (Area of Interest, Time of Interest and Cloud Coverage).Cloud Coverage layers (e.g. spread of the clouds, cloud types);- Sun elevation angle together with topography information.Radiometric/geometric image quality.Simple pre-classification (e.g. snow cover). The project shall finally produce a prototype, envisaging some pre-defined operational scenarios (e.g. Corine Land Cover mapping, Urban Atlas mapping, Control with Remote Sensing campaigns which routinely take place in Europe) to clearly demonstrate its usefulness and effectiveness.The current user workflow for EO images exploitation, which puts a non-negligible burden on the user side (download and analysis of datasets before actual processing) will consistently benefit from such a Suitability Coverage Engine, which will easily provide optimal datasets suitable for successive mapping/classification task, avoiding the transfer of useless data.