General information

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.

Members

Prof. Dr. – Ing. Morris Riedel

Morris Riedel is an Associate Professor at the School of Engineering and Natural Sciences of the University of Iceland. He received his PhD from the Karlsruhe Institute of Technology (KIT) and works in parallel and distributed systems since 15 years. He previously held various positions at the Juelich Supercomputing Centre of Forschungszentrum Juelich in Germany. At this institute, he is also the head of a specific scientific research group focused on ‘High Productivity Data Processing’ and a cross-sectional team ‘Deep Learning’.

Dr. -Ing. Gabriele Cavallaro

Gabriele Cavallaro received the B.Sc. and M.Sc. degrees in telecommunications engineering from the University of Trento, Italy, in 2011 and 2013, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Iceland, Iceland, in 2016. He is currently the deputy head of the ‘‘High Productivity Data Processing’’ (HPDP) research group at the Jülich Supercomputing Centre, Germany. 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.

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 high performance and distributed systems. He serves on the scientific committees of several international conferences and he is a referee for numerous international journals. Since 2019 he gives lectures on scalable machine learning for remote sensing big data at the Institute of Geodesy and Geoinformation, University of Bonn, Germany.

Ing. Rocco Sedona

Rocco Sedona received the B.Sc. and M.Sc. degrees in information and communications engineering from the University of Trento in 2016 and 2019, respectively. He is member of the ‘‘High Productivity Data Processing’’ (HPDP) research group at the Jülich Supercomputing Centre, Germany. He is currently pursuing the Ph.D. degree in computational engineering at the University of Iceland. His research interest is mainly in machine learning methods for remote sensing applications, with a particular focus on distributing deep learning models on multiple GPUs of High Performance Computing (HPC) systems.

Surbhi Sharma

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.

Events

Summer school 2021

The summer school is organized by the IEEE GRSS HDCRS Working group with the support of the SimDataLab RS. Each year the school focuses on specific subjects that are included in the topics addressed by HDCRS working group.

More information

Publications

Journals

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.

Conference Proceedings

R. Sedona, G. Cavallaro, M. Riedel and M. Book, ”Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021 (Under review).

M. Riedel, G. Cavallaro and J. A. Benediktsson, ”Practice and Experience in using Parallel and Scalable Machine Learning in Remote Sensing from HPC over Cloud to Quantum Computing”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021 (Under review).

A. Delilbasic, G. Cavallaro, F. Melgani, M. Riedel and K. Michielsen, ”Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021 (Under review).

D. Coquelin, R. Sedona, M. Riedel and M. Goetz, ”Evolutionary Optimization of Neural Architectures in Remote Sensing Classification Problems”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021 (Under review).

G. Cavallaro, D. Willsch, M. Willsch, K. Michielsen, and M. Riedel, ”Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-WAVE Quantum Annealer”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020 (Press).

R. Sedona, G. Cavallaro, J. Jitsev, A. Strube, M. Riedel, and M. Book, ”Scaling up a Multispectral Resnet-50 to 128 GPUs”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020 (Press).

R. Zhang, G. Cavallaro, and J. Jitsev, ”Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020 (Press).

E. Erlingsson, G. Cavallaro, H. Neukirchen, and M. Riedel, ”ScalableWork ows for Remote Sensing Data Processing with the Deep-Est Modular Supercomputing Architecture”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5905-5908, 2019.

G. Cavallaro, V. Kozlov, M. Goetz, and M. Riedel, ”Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems“, in Proceedings of the conference on Big Data from Space (BiDS), pp. 177-180, 2019.

E. Erlingsson, G. Cavallaro, M. Riedel, and H. Neukirchen, ”Scaling Support Vector Machines Towards Exascale Computing for Classi cation of Large-Scale High-Resolution Remote Sensing Images“, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1792-1795, 2018.

E. Erlingsson, G. Cavallaro, A. Galonska, M. Riedel, and H. Neukirchen, ”Modular Supercomputing Design Supporting Machine Learning Applications“, in 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0159-0163, 2018.

S. Memon, G. Cavallaro, B. Hagemeier, M. Riedel, and H. Neukirchen, ”Automated Analysis of Remotely Sensed Images Using the UNICOREWork ow Management System“, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2153-7003, 2018.

S. Memon, G. Cavallaro, M. Riedel, and H. Neukirchen, ”Facilitating Efficient Data Analysis of Remotely Sensed Images Using Standards-Based Parameter Sweep Models”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3680-3683, 2017.

M. Goetz, M. Richerzhagen, C. Bodenstein, G. Cavallaro, P. Glock, M. Riedel, and J. A. Benediktsson, ”On Scalable Data Mining Techniques for Earth Science”, International Conference On Computational Science, vol. 51, pp. 2188-2197, 2015.

G. Cavallaro, M. Riedel, M. Goetz, C. Bodenstein, M. Richerzhagen, P. Glock, and J. A. Benediktsson, ”Scalable Developments for Big Data Analytics in Remote Sensing”, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1366-1369, 2015.