The Simulation and Data Lab Remote Sensing (SimDataLab RS) leads to increase the visibility on interdisciplinary research between remote sensing and advanced computing technologies and parallel programming. This includes high-performance and distributed computing, quantum computing and specialized hardware computing. The SimDataLab RS is based at the University of Iceland and works together with the High-performance and Disruptive Computing in Remote Sensing (HDCRS) working group of the Geoscience and Remote Sensing Society (GRSS). Together with HDCRS, the SimDataLab RS disseminates information and knowledge through educational events, special sessions and tutorials at conferences and publication activities.
High-Performance Computing for Remote Sensing
Specialized in harnessing distributed high-performance computing systems to process and analyze vast remote sensing datasets, ensuring that big data challenges are addressed with cutting-edge solutions.
Innovative Machine Learning & Quantum Computing
At the forefront of integrating advanced machine learning algorithms, ranging from deep learning networks to quantum computing techniques, for remote sensing applications. The focus is on pushing the boundaries of data classification and analysis using these innovative methodologies.
Scalable Data Processing Systems
Committed to the development of scalable and modular data processing systems, designed to efficiently handle the exponential growth of remote sensing data. The emphasis is on ensuring algorithms and methods can be effectively scaled to accommodate any dataset size.
Prof. Dr. -Ing. Gabriele Cavallaro
Gabriele Cavallaro (Member, IEEE) received his B.Sc. and M.Sc. degrees in Telecommunications Engineering from the University of Trento, Italy, in 2011 and 2013, respectively, and a Ph.D. degree in Electrical and Computer Engineering from the University of Iceland, Iceland, in 2016. From 2016 to 2021 he has been the deputy head of the “High Productivity Data Processing” (HPDP) research group at the Jülich Supercomputing Centre, Germany. From 2019 to 2021 he gave lectures on scalable machine learning for remote sensing big data at the Institute of Geodesy and Geoinformation, University of Bonn, Germany. Since 2022, he is the Head of the “AI and ML for Remote Sensing” Simulation and Data Lab at the Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany and an Adjunct Associate Professor with the School of Natural Sciences and Engineering, University of Iceland, Iceland. He is also the Chair of the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group of the IEEE GRSS ESI Technical Committee and a Visiting Professor at the Φ-lab of the European Space Agency (ESA) in the context of the Quantum Computing for Earth Observation (QC4EO) initiative. Since October 2022 he serves as an Associate Editor of the IEEE Transactions on Image Processing (TIP). He also serves on the scientific committees of several international conferences and he is a referee for numerous international journals. He was the recipient of the IEEE GRSS Third Prize in the Student Paper Competition of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015 (Milan – Italy). His research interests cover remote sensing data processing with parallel machine learning algorithms that scale on distributed computing systems and cutting-edge computing technologies, including quantum computers.
Prof. Dr. – Ing. Morris Riedel
Morris Riedel received his PhD from the Karlsruhe Institute of Technology (KIT) and worked in data-intensive parallel and distributed systems since 2004. He is currently a Full Professor of High-Performance Computing with an emphasis on Parallel and Scalable Machine Learning at the School of Natural Sciences and Engineering of the University of Iceland. Since 2004, Prof. Dr. – Ing. Morris Riedel held various positions at the Juelich Supercomputing Centre of Forschungszentrum Juelich in Germany. In addition, he is the Head of the joint High Productivity Data Processing research group between the Juelich Supercomputing Centre and the University of Iceland. Since 2020, he is also the EuroHPC Joint Undertaking governing board member for Iceland. His research interests include high-performance computing, remote sensing applications, medicine and health applications, pattern recognition, image processing, and data sciences, and he has authored extensively in those fields. Prof. Dr. – Ing. Morris Riedel online YouTube and university lectures include High-Performance Computing – Advanced Scientific Computing, Cloud Computing and Big Data – Parallel and Scalable Machine and Deep Learning, as well as Statistical Data Mining. In addition, he has performed numerous hands-on training events in parallel and scalable machine and deep learning techniques on cutting-edge HPC systems.
Amer Delilbasic (Student Member, IEEE) received the B.Sc. and M.Sc. degrees in information and communication engineering from the University of Trento in 2019 and 2021, respectively. He is member of the “AI and ML for Remote Sensing” Simulation and Data Lab at the Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany. He is currently pursuing the Ph.D. degree in computational engineering at the University of Iceland. He is an external researcher at Φ-lab, European Space Agency, Frascati, Italy. His research interest is mainly in machine learning methods for remote sensing applications, with a particular focus on Quantum Computing (QC) and High Performance Computing (HPC).
Dr. Ing. Rocco Sedona
Rocco Sedona holds B.Sc. and M.Sc. degrees in information and communications engineering from the University of Trento, awarded in 2016 and 2019, respectively. In 2023, he defended his doctoral thesis in Computational Engineering at the University of Iceland.A dedicated member of the 'High Productivity Data Processing' (HPDP) research group at the Jülich Supercomputing Centre in Germany, Rocco's research focus predominantly revolves around developing machine learning methods for remote sensing applications. His primary interest is optimizing and distributing deep learning models across multiple GPUs within High-Performance Computing (HPC) systems.Rocco assumes the role of Co-chair for the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group within the IEEE GRSS ESI Technical Committee.
Edoardo Pasetto received his Bachelor’s degree and Master’s degree in Information and Communications Engineering from the University of Trento in 2019 and 2021, respectively. He is currently doing a PhD with the RWTH university of Aachen and Forshungszentrum Jülich on quantum computing with a main focus on hybrid quantum-classical applications.
Joseph Xavier Arnold
Joseph Xavier Arnold is from Bengaluru, India. After his bachelors degree in computer science and engineering he began his career as a web application developer, programming primarily in J2EE and Scala. After his post graduation, he has been working on program optimization mainly targeting math libraries on the AMD64 architecture. He enjoys programming and is currently exploring parallel programming and distributed deep learning in high performance computing systems.
Liang Tian received his B.Sc. degree and M.Sc. in Electrical Engineering and Information Technology from the Karlsruhe Institute of Technology and Technical University of Munich in 2019 and 2022, respectively. He is currently pursuing the PH.D. degree in computational engineering at the University of Iceland. His research interest lies mainly in deep learning methods for remote sensing applications with the combination of High Performance Computing (HPC) systems.
Surbhi Sharma received the B.Tech degree in electronics and communication engineering from Amity University, India, in 2015, and the M.Sc degree in Geo-information science and Earth Observation with a specialization in Geo-informatics jointly from the Indian Institute of Remote Sensing, Indian Space and Research Organization (ISRO), India, and the Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, the Nethelands, in 2018. She is a member of the “High Productivity Data Processing” (HPDP) research group at the Jülich Supercomputing Centre, Germany. She is currently pursuing the Ph.D. degree in computational engineering at the University of Iceland. Her research interest lies in scalable machine learning and deep learning methods for remote sensing applications, with a particular focus on advanced deep transfer learning methods using modern High Performance Computing (HPC) systems.
Prof. Dr. – Ing. Jón Atli Benediktsson
Jón Atli Benediktsson received the Cand.Sci. degree in electrical engineering from the University of Iceland, Reykjavik, in 1984, and the M.S.E.E. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1987 and 1990, respectively. Since July 1, 2015 he is the President and Rector of the University of Iceland. From 2009 to 2015 he was the Pro Rector of Science and Academic Affairs and Professor of Electrical and Computer Engineering at the University of Iceland. His research interests are in remote sensing, biomedical analysis of signals, pattern recognition, image processing, and signal processing, and he has published extensively in those fields. Prof. Benediktsson is a Highly Cited Researcher (Clarivate Analysis, 2018-2020). He was the 2011-2012 President of the IEEE Geoscience and and Remote Sensing Society (GRSS) and was on the GRSS AdCom from 2000-2014. He was Editor in Chief of the IEEE Transactions on Geoscience and Remote Sensing (TGRS) from 2003 to 2008 and has served as Associate Editor of TGRS since 1999.
R. Sedona, G. Cavallaro, J. Jitsev, A. Strube, M. Riedel, and J. A. Benediktsson, ”Remote Sensing Big Data Classification with High Performance Distributed Deep Learning”, Remote Sensing, vol. 11, no. 24, pp. 3056, 2019.
J. M. Haut, J. A. Gallardo, M. E. Paoletti, G. Cavallaro, J. Plaza, A. Plaza, and M. Riedel, ”Cloud Deep Networks for Hyperspectral Image Analysis”, IEEE Transactions on Geoscience and Remote Sensing”, vol. 57, no. 12, pp. 9832-9848, 2019.
M. Goetz, G. Cavallaro, T. Geraud, M. Book, and M. Riedel, ”Parallel Computation of Component Trees on Distributed Memory Machines“, IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 11, pp. 2582-2598, 2018.
G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson, and A. Plaza, ”On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS), vol. 8, no. 10, pp. 4634-4646, 2015.
R. Sedona, C. Paris, G. Cavallaro, L. Bruzzone, and M. Riedel, “A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 14, pp. 10134–10146, 2021, https://doi.org/10.1109/JSTARS.2021.3115604
S. Moreno-Álvarez, M. E. Paoletti, G. Cavallaro, J. A. Rico and J. M. Haut, "Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing," in IEEE Geoscience and Remote Sensing Letters (GRSL), no. 3512205, vol. 19, pp. 1-5, 2022, https://doi.org/10.1109/LGRS.2022.3173052
B. Zhao, H. I. Ragnarsson, M. O. Ulfarsson, G. Cavallaro and J. A. Benediktsson, "Predicting Classification Performance for Benchmark Hyperspectral Datasets," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), vol. 15, pp. 4180-4193, 2022, https://doi.org/10.1109/JSTARS.2022.3173893