PROJECT TITLE: Content Based Query Concept for Exploration and Discovery of Information in Earth Observation and Medical Libraries — CONCEDE
Coordinator: University Politehnica of Bucharest, Research Center for Spatial Information, UPB — CeoSpaceTech
Partners: SC Advanced Studies and Research Center SRL (ASRC)
Period: 19 November 2012 – 18 November 2015
Project director: Corina Vaduva
Project team: The project team includes 1 Senior Scientific Researcher, 2 Scientific Researchers and 2 PhD student from UPB-CeoSpaceTech and 5 Scientific Researchers from ASRC.
Description: The project will exploit the full scope of image processing, database management and human knowledge in order to define an applicative framework to explore heterogeneous data resources, resulting in the discovery of semantic information hidden in image libraries. The project goal is to develop a demonstrator, a query engine concept widely applicable for several types of data, in order to validate and evaluate the acpabilities of the proposed content based information retrieval concept. The architecture will be modular, such that the concept can be anytime updated with new algorithms and methods.
- Contributions to European Space Agency (ESA) research activities
- Contributions to scientifically development
- Contribution to new applications solutions development
- WP1: Project Management and Reporting
- WP2: Analysis and Review
- WP3: CONCEDE Demonstrator Concept
- WP4: CONCEDE Demonstrator
- WP5: CONCEDE Demonstrato Elaboration
- WP6: Validation and Evaluation
Contributions to the STAR programme objectives:
- CONCEDE project aims at enhacing the functionalities of the Earth Observation (EO) Payload Ground Segment systems and the next-generation services for an enhanced data content exploration with focus on Sentinel 1 and 2.
- Development of a new generation of data mining (DM) tools and knowledge discovery in databases (KDD) concepts and algorithms based on data compression in order to simplify the feature extraction process and to avoid the information loss between low level and high level characteristics.
- Develop a new modular concept, with a general architecture framework which will allow periodic update with newly implemented methods and algorithms further developed for specific data.
- This project opens new opportunities to improve the technology readiness level for Big Data analytics.
- Project management is made according to ESA templates and rules.