Space Data Management

Agostino Cortesi, Ph.D., is a full professor of Computer Science at Ca’ Foscari University, Venice, Italy. He has held the position of visiting professor at several universities and research centers, including Ecole Normale Supérieure Paris, Ecole Polytechnique Palaiseau, IBM T.J.Watson Research Center, and University of Calcutta. His main research interests concern software engineering and static analysis techniques, with particular emphasis on security issues. He has also investigated the impact of semantics-based approaches in different applicative scenarios (watermarking, relational databases, robotics, etc.). He published more than 150 papers in high-level international journals (including ACM TOPLAS and IEEE TSE) and proceedings of international conferences (including ACM POPL, ACM PLDI, and IEEE LICS). His current h-index is 23 according to Scopus and 31 according to Google Scholar. Currently, he serves as a co-editor-in-chief of the book series “Services and Business ProcessReengineering” published by Springer-Nature.

• Digital Innovation in Space Science.- Legal regimes for Space data.- Regulating space data management.- High Performance Computing, AI, the Metaverse and quantum computing to transform space data management.- AI applications of Space Data.- Blockchain-enabled Satellite Onboard Computing for Smart Contract: Benefits for Multi-sided Markets and IoT Applications.- On the use of available space monitoring data for improving the risk of collision avoidance.- Optimising collection, transmission and transformation of space data to take up security challenges.- A Fragile Watermarking Approach for Earth Observation Data Integrity Protection.- Deepness: Deep Neural Remote Sensing QGIS Plugin.- Earth Observation Data Transfer at Limitations of Small Satellite Missions.- Earth Observation Data Management and Offer: the experience of the ARIC.- Using satellite data to estimate NO2 concentrations in mainland UK from 2005 to 2021.- Integration of Terrestrial and Non-Terrestrial Networks: challenges and perspectives within the RESTAR project.- Estimate the ice volume of Earth’s glaciers with deep learning and remote sensing.- Can life exist without water? A data driven approach.