Begüm Demir is a Professor and Head of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin (TU Berlin), Germany. From 2013 to 2017, she was an Assistant Professor at the Department of Computer Science and Information Engineering, University of Trento, Italy, while in 2017 she became an Associate Professor at the same department. She received the B.S. degree in 2005, the M.Sc. degree in 2007, and the Ph.D. degree in 2010, all in Electronic and Telecommunication Engineering from Kocaeli University, Turkey. Her main research interests include image processing and machine learning with applications to remote sensing image analysis. She was a recipient of a Starting Grant from the European Research Council (ERC) with the project “BigEarth-Accurate and Scalable Processing of Big Data in Earth Observation” in 2017, and the “2018 Early Career Award” presented by the IEEE Geoscience and Remote Sensing Society. Through the BigEarth project, her team has recently developed and made public one of the largest Sentinel-2 benchmark archive (BigEarthNet) to drive the deep learning studies in remote sensing.
Dr. Begüm Demir is a senior member of IEEE since 2016. She is a Scientific Committee member of the Conference on Big Data from Space, Living Planet Symposium, International Joint Urban Remote Sensing Event and SPIE International Conference on Signal and Image Processing for Remote Sensing. She is the founder and the co-chair of Image and Signal Processing for Remote Sensing Workshop organized within the IEEE Conference on Signal Processing and Communications Applications since 2014.
Machine Learning for Information Discovery from Big Earth Observation Data Archives
During the last decade, a huge number of earth observation (EO) satellites with optical and Synthetic Aperture Radar sensors onboard have been launched and advances in satellite systems have increased the amount and variety of EO data. This has led to massive EO data archives with huge amount of remote sensing (RS) images, from which retrieving useful information in the framework of disaster monitoring and management is challenging. In this talk, a general overview on scientific and practical problems related to RS image characterization, indexing and search from massive archives will be initially discussed. Then, recent developments that can overcome the considered problems will be introduced by focusing on scalable and accurate remote sensing image search and retrieval systems for disaster management.