EO Spatio-Temporal Patterns Extraction
Since the very beginning of satellite remote sensing the methods and applications the Satellite Image Time Series (SITS) are the main nature of Earth Observation. Presently, with the regular observations and free and open access of the Copernicus data the impact of SITS is largely amplified. The challenges of the EO Big Data are critically accentuated due to joint volume explosion, high acquisition velocity and sensor variety.
The presentation emphases on novel Artificial Intelligence (AI) paradigms focuses to convert the SITS in valuable EO products with impact in new applications for understanding of the Erath cover spatio-temporal processes over long periods of time. AI for EO is largely an interdisciplinary field and involves the convergence of very different methods. The lecture overviews and discuss specific topics for SITS regarding the orbit, mission, sensor constellations, intelligent agents, machine learning, deep learning, data indexing, data bases, and DNN.
Mihai Datcu received the M.S. and Ph.D. degrees in Electronics and Telecommunications from the University Politechnica Bucharest UPB, Romania, in 1978 and 1986. In 1999 he received the title Habilitation à diriger des recherches in Computer Science from University Louis Pasteur, Strasbourg, France. Currently he is Senior Scientist and Data Intelligence and Knowledge Discovery research group leader with the Remote Sensing Technology Institute (IMF) of the German Aerospace Center (DLR), Oberpfaffenhofen, and Professor with the Faculty of Electronics, Telecommunications and Information Technology, UPB. His interests are in Data Science, Machine Learning and Artificial Intelligence, and Computational Imaging for space applications. He is involved in Big Data from Space European, ESA, NASA and national research programs and projects. He is a member of the ESA Big Data from Space Working Group. He is IEEE Fellow. He is holder of a 2017 Blaise Pascal International Chair at CEDRIC, CNAM.