Artificial Intelligence for Very High Resolution Earth Observation: Environment Monitoring – Mihai Datcu 05 Apr. 2019

When: Friday 5 April 2019 at 14:30
Where: Campus Pierre and Marie Curie (Sorbonne University) room 105 of LIP6 corridor 25-26 1st floor.

This presentation is organised in collaboration with the Chaire Internationale de Recherche Blaise Pascal financed by « Région Ile-de-France », managed by the « Fondation de l’Ecole normale supérieure » and hosted at CEDRIC, Cnam.

The Earth is facing unprecedented climatic, geomorphologic, environmental and anthropogenic changes, which require global scale observation and monitoring. Thus a multitude of new orbital and suborbital Earth Observation (EO) sensors and mission are in operation or will be soon launched. The interest is in a global understanding involving observation of large extended areas, and long periods of time, with a broad variety of EO sensors.  The collected EO data volumes are thus increasing immensely with a rate of many Terabytes of data a day. With the current EO technologies these figure will be soon amplified, the horizons are beyond Zettabytes of data. The challenge is the exploration of these data and the timely delivery of focused information and knowledge in a simple understandable format.Therefore, search engines, and Data Mining are new fields of study that have arisen to seek solutions to automating the extraction of information from EO observations and other related sources that can lead to Knowledge Discovery and the creation of an actionable intelligence. Knowledge Discovery is among the most interesting research trends, however, the real challenge is to combine Artificial Intelligence with the power and potential of human intelligence, this being a primary objective in the field of Human Machine Communication (HMC). The goal is to go beyond the today methods of information retrieval and develop new concepts and methods to support end users of EO data to interactively analyze the information content, extract relevant parameters, associate various sources of information, learn and/or apply knowledge and to visualize the pertinent information without getting overwhelmed. In this context, the synergy of HMC and information retrieval becomes an interdisciplinary approach in automating EO data analysis.

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 Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, UPB. From 1992 to 2002 he had a longer Invited Professor assignment with the Swiss Federal Institute of Technology, ETH Zurich. From 2005 to 2013 he has been Professor holder of the DLR-CNES Chair at ParisTech, Paris Institute of Technology, Telecom Paris. 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 received in 2006 the Best Paper Award, IEEE Geoscience and Remote Sensing Society Prize, in 2008 the National Order of Merit with the rank of Knight, for outstanding international research results, awarded by the President of Romania, and in 1987 the Romanian Academy Prize Traian Vuia for the development of SAADI image analysis system and activity in image processing. He is IEEE Fellow. He is holder of a 2017 Blaise Pascal Chair at CEDRIC, CNAM.

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