Researchers from the FIT are working on a new platform that will help analyse space data

The technology that is being developed at the FIT within the BLENDED project helps the European Space Agency (ESA) process images of Earth. The project connects scientists across Europe in an effort to create a revolutionary platform for distributed and, most importantly, secure data processing using artificial intelligence that processes and analyses space data.

How can we share results from space-based observation and maintain the integrity and security of such data? In the following year, researchers from the Department of Information Systems of the Faculty of Information Technology of BUT will help the European Space Agency answer this question as part of the international research project named Blockchain Enabled Deep Learning Data Analysis (or BLENDED in short). Apart from the FIT BUT, the project participants include Belgian company SpaceApplications, IT4Innovations National Supercomputer Centre in Ostrava, as well as Greek partners Forth (Foundation for Research and Technology Hellas research centre) and Geosystem Hellas (company specialised in processing of geodetic data).

Together, the institutions will be working on solving one of the long-term scientific projects of the European Space Agency which focuses on the creation of a platform that will use machine learning for analysis of space data. Over the years of its existence, ESA has used its satellites to gather vast amounts of data and images of different places on Earth. This data is freely available for universities and companies to subsequently analyse, process and use to reach interesting conclusions - for example the rates of drying out of soil, the rate of urbanisation or the fertility farmlands.

Processing of these terabytes of data in real time is very difficult, therefore, artificial intelligence is nowadays used to facilitate this task. It is quite easy to create an algorithm solving a certain problem with respect to one specific dataset; however, to adapt and successfully use such algorithm on thousands of different datasets requires the use of AI. The research team from the Department of Information Systems of the FIT BUT will take up the challenge of finding the best way to share the results of such analyses, e.g. normalised data, extracted photometric layers or trained AI models. Together, they have designed and are implementing a platform that would make it possible to do just that. The platform is based on two complementary technologies - InterPlanetary File System (IPFS) and Ethereum. 

The NES@FIT research group participates in the project. Members of the group are Vladimír Veselý, Dušan Kolář, Ondřej Lichtner, Michal Koutenský, Dominika Regéciová and Matúš Múčka. They have vast experience with cryptocurrencies, blockchain technology, smart contracts, and distributed systems in general and they are developing a platform that has significantly larger potential than just the required use within the ESA project. The platform can work as a sort of undercarriage for any completely distributed system (in terms of data storage, computing, and system management). So we are very much looking forward to seeing how the know-how and experience acquired by the NES@FIT team pay out in other grant opportunities or different contractual research projects.

The co-operation with the research partners within the project will continue until mid-2021 when the deployment of the platform prototype should be completed which will enable the following:

• upload and (securely) share any data within a potentially unlimited storage;
• run series of highly demanding AI calculations (both third-party and the participants' own) using the data stored in datacentres participating in the project;
• subsequently publish (in the IPFS) the results of such calculations, algorithms used and the AI models trained, as data for which it is possible to verify the origin and authorship (via Ethereum blockchain) so that the integrity (and confidentiality, if needed) of such data is maintained in the entire chain of custody.