This event was held on 11th of December and was within the frame of IEEE GRSS Slovenian Chapter activities, founded by the IEEE GRSS Chapter Budget awarded for 2023.
INVITATION
to the event
IEEE GRSS Slovenia Chapter Day 2023
Keynote talk:
Prof. Dr. Mihai Datcu,
IEEE fellow, IEEE GRSS Distinguish Lecturer
December 11, 2023
Faculty of Electrical Engineering and Computer Science, University of Maribor,
Room G2-Beta
We kindly invite you to the event IEEE GRSS Slovenia Chapter Day event on Monday, December 11, 2023, at the Faculty of Electrical Engineering and Computer Science, Koroška cesta 46, Maribor, Slovenia. The sponsors and organizers of the event are the IEEE GRSS Slovenia Chapter and the Laboratory for Signal Processing and Remote Control at the Faculty of Electrical Engineering and Computer Science, University of Maribor.
The main part of the event will be the keynote talk, given by Prof. Dr. Mihai Datcu, IEEE Fellow, IEEE GRSS Distinguish Lecturer.
https://feri.um.si/en/
Peter Planinšič
IEEE Slovenia Chapter Chair
Dušan Gleich
Head of Laboratory for Signal Processing and Remote Control,
Faculty of Electrical Engineering and Computer Science, University of Maribor
Keynote talk
Digital Twin Earth for climate change adaptation: from AI to Quantum Machine Learning based solutions
Prof. Dr. Mihai Datcu, IEEE Fellow, IEEE GRSS Distinguish Lecturer
German Aerospace Technology (DLR), Romanian Space Agency
Despite the permanent effort to reduce emissions and achieve carbon neutrality a warmer climate is no longer to be avoided. The adaptation to climate change should build resilience in the next decades at global scale. Satellite remote sensing is the only global and continuous Earth Observation (EO) technology presently contributing to the elaboration of climate models describing and predicting changes at scales of hundreds of kilometres for periods of months to years. However, the adaptation measures shall be applied at human activities scales, from 10m to 1km and from periods of days to months. The new problematic is the elaboration of coupled models across spatio-temporal scales, therefore involving the use of very high spatial resolution and dense time series from multiple EO missions. This is the new challenge of Big EO Data.
The digital and sensing technologies, i.e. Big Data, are revolutionary developments massively impacting the Earth Observation (EO) domains. While, Artificial Intelligence (AI) is providing now the methods to valorize the Big Data. Today the accepted trends assume more data we analyze, the smarter the analysis paradigms will perform. However, the data deluge, diversity, or the broad range of specialized applications are posing new major challenges. From the methodological side the challenges are related to, the reproducibility, the trustworthiness, physics awareness, and over all, the explainability of the methods and results. At present, quantum computing and AI are the key technologies in the digital era. The progress and transfer of quantum resources for use in practical applications is in constant acceleration. Quantum computing, quantum annealing, quantum circuits, or simulators for quantum computing are currently easily accessible. In this context the presentation will address aspects of quantum machine learning for EO, with the goal to identify if a quantum algorithm may bring any advantage compared with classical methods.
The presentation covers the major developments, of hybrid, physics aware AI paradigms, at the convergence of forward modelling, inverse problem and machine learning, to discover causalities and make prediction for maximization of the information extracted from EO and related non-EO data. The majority of EO applications or services require the complementary EO multi-sensor and non-EO data, i.e., sensor fusion and multitemporal observations.