• Learning outcomes
Welcome at the University of Iceland and Opening of the Summer School
Welcome at the University of Iceland and opening of the summer school with an introduction to the IEEE Geoscience and Remote Sensing Society (GRSS).
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 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.
Work and Activities of the HDCRS Working Group
This presentation will introduce the working group “High-performance and Disruptive Computing in Remote Sensing” (HDCRS) of the GRSS Earth Science Informatics Technical Committee (ESI TC). HDCRS is the organizer of this summer school and its main objective is to connect a community of interdisciplinary researchers in remote sensing who are specialized on high-performance and distributed computing, disruptive computing (e.g., quantum computing) and parallel programming models with specialized hardware (e.g., GPUs, FPGAs). The activities of the working group include educational events, special sessions and tutorials at conferences and publication activities, which will be presented.
Dora Blanco Heras
Dora B. Heras is an associate professor in the Department of Electronics and Computer Engineering at the University of Santiago de Compostela (Spain). She received a MS in Physics in 1993 and was awarded a PhD cum laude from this university. In the period from 2005 to 2010 she was appointed as the head of the Sustainable Development Office at this university. Since 2008 she is also with the research centre CiTIUS (Centro de Investigación en Tecnoloxías Intelixentes) where she leads the hyperspectral remote sensing computing line and has received the accreditation as full professor in 2020. He is also co-chair of the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group of the IEEE GRSS ESI Technical Committee.
Her research contributions cover a range of topics in the combined fields of image processing, remote sensing, machine learning and high performance computing. In particular, in the last ten years her research has been framed in the line of high performance computing and its application to remote sensing. She has participated in research projects funded by Spanish and European institutions, and R&D agreements. She has served as program committee, guest editor and reviewer in several conferences, in particular, the Euromicro 2021 Parallel and Distributed Conference, and serves as reviewer for different top-ranked journals. She is also a member of the Euro-Par conference Steering Committee since 2018 and has acted as co-chair of the co-located workshops for all the editions since 2017.
Gabriele Cavallaro 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.
Lowering the Barrier for Modern Cloud-based Geospatial Big Data Analysis by Combined Use of Innovative and Traditional Infrastructure
Geospatial data is getting bigger and such large and complex datasets are becoming more and more difficult to process by using traditional systems and methods, such as individual workstations. Numerous spatial computing solutions have been developed to tackle this challenge by enabling distributed data stores, parallel and distributed computing capabilities, and special computing units (e.g., GPU/TPU) to enable discovery, delivery, analysis, and visualisation of geospatial data. However, these solutions require specialized know-how and expertise, as well as access to adequate computing infrastructure that is mostly located remotely in the Cloud. Therefore, a transition in modus operandi is necessary. The Geospatial Computing Platform lowers the barrier by providing a state-of-the-art computing infrastructure designed for big geospatial analysis tasks that combines low-energy, high-performance Edge AI units with powerful GPU-enabled big data computing units in a seamless and innovative manner. Through the platform the users can access thousands of scientific software packages (e.g. Python / R) that are kept up to date regularly. Public datasets available platform-wide improve data access and reduce data duplication, whereas shared workspaces allow research groups to work in a collaborative manner. Beside a modern interactive notebook interface, the platform also allows remote desktop access for desktop applications (e.g., QGIS, SNAP) and features integrated geospatial database, map serving, and data collection services to benefit from existing well-established tools and technologies. This talk will provide information about the design and architecture of the platform, current use cases, and lessons learned during the operation period of two years, involving 250,000+ hours of multi-core/GPU computation and a user community of more than 800 users.
Dr. Serkan Girgin has established and is currently leading the activities of the Center of Expertise in Big Geodata (CRIB) at the Faculty of Geo-Information Science and Earth Observation (ITC) of University of Twente, the Netherlands. CRIB is an overarching facility performing research and collecting, developing, and sharing operational know-how on the geospatial big data technologies. Dr. Girgin performs research on performance and effective use of big data, cloud computing, and research data management tools and technologies, and provides advice and consultancy on their adoption for education, research, and capacity development activities. He is also an expert on the design and development of geocomputing platforms, GIS and RS applications, environmental information systems, and large-scale web applications. He has designed and developed ITC's Geospatial Computing Platform, and European Commission's Natech Database (eNatech) and Rapid Natech Risk Assessment and Mapping System (RAPID-N). He has M.Sc. and Ph.D. degrees in Environmental Engineering, a second M.Sc. degree in Geodetic and Geographic Information Technologies, and more than two decades of research and consultancy experience in academic, private, and scientific organizations since 1996, including European Commission Joint Research Centre and Space Technological Research Institute of TUBITAK. He is an eScience Center Fellow and SURF Research Support Champion in the Netherlands in 2022.
Hyperspectral technology: inspiring ideas, challenges and opportunities
Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, going from precision agriculture to environmental science or health, just to name some. Since its early development, in the 1970's, these techniques have experienced an enormous progress due to the improvements seen in electronics, computing and software throughout these years, becoming one of the most powerful tools for acquiring information in several fields. Whereas conventional Red, Green, Blue (RGB) cameras are only able to capture light in the visible range, providing information exclusively for three spectral bands, hyperspectral sensors can divide the spectrum into many more contiguous bands, generating an image of the scene under observation with information across a much wider range of the electromagnetic spectrum for each pixel. The main advantage of this technology is that certain materials, when exposed to light, reflect a unique spectral signature in the electromagnetic spectrum, making it easier to identify the elements that compose the scene under analysis when the right techniques are applied, and hence serving for a plethora of applications. However, although the advantages are immense, the implementation of systems based on hyperspectral sensors poses some major challenges, due to the huge amount of information involved and because of the different nature of sensors. Especially real time processing of information taken from hyperspectral pushbroom sensors, widely used because they provide good spectral and spatial resolution, are complex because spectral information is received before spatial one.
In this seminar, the basis of hyperspectral sensors technology and processing methods will be explained, focusing on the systems implementations based on embedded systems. Examples of different applications using embedded high-performance computing (based on GPUs or FPGAs), in which the instructors have been involved for the past 10 years, will be presented. The applications are specific cases of remote sensing, i.e., the integration of hyperspectral cameras in UAVs (Unmanned Aerial Vehicles) of different types and sizes; or in earth observation satellites such as the CHIME instrument (Copernicus Hyperspectral Imaging Mission for the Environment) for next Copernicus Mission. Moreover, other non-remote sensing applications of hyperspectral images such as to medicine will be presented. Due to the limit of time, it will not be an extremely detailed talk but with potential enough to give rise to new inspiring ideas in which your imagination will be the limit.
José Francisco López
José Fco. López received the M.S. degree in physics from the University of Seville, Spain, and the Ph.D. degree from the University of Las Palmas de Gran Canaria (ULPGC), Spain. He has conducted his investigations at the Research Institute for Applied Microelectronics (IUMA), where he was Deputy Director from 2009 to 2019. He currently lectures at the School of Telecommunication and Electronics Engineering, the School of Industrial Engineering and the M.Sc. and PhD Program of IUMA, in the ULPGC. He was with Thomson Composants Microondes, Orsay, France, in 1992. In 1995, he was with the Center for Broadband Telecommunications, Technical University of Denmark, Lyngby, Denmark, and in 1996-2000, he was a Visiting Researcher at the Edith Cowan University, Perth, Western Australia, and the University of Adelaide, Australia. His main areas of interest include the field of image processing, UAVs, hyperspectral technology and their applications. Dr. Lopez has been actively enrolled in more than 40 research projects funded by the European Community, Spanish Government, and international private industries. He has written around 150 papers in national and international journals and conferences. Presently he is Vice-President of the Aeronautics and Aerospace Cluster at the Canary Islands, and is actively enrolled in different initiatives with the local and national government to create aerospace facilities in the region.
Roberto Sarmiento is Full-Professor at the Electronics and Telecommunication Engineering School at University of Las Palmas de Gran Canaria, Spain, in the area of Electronic Engineering. He contributed to set this school up, he was the Dean of the Faculty from 1994 to 1998 and Vice-Chancellor for Academic Affairs and Staff at the ULPGC from 1998 to 2003. He is a co-founder of the Research Institute for Applied Microelectronics (IUMA) and Director of the Integrated Systems Design Division of this Institute. He has published more than 90 journal papers and more than 180 conference papers. He has participated in more than 70 projects and research programmes funded by public and private organizations. He has led several projects for the European Space Agency (some of them related to development of IPs for ESA´s portfolio of CCSDS 123 and CCSDS 121 standards) and has collaborations with main companies in the sector, such as Thales Alenia Space, SENER, GMV, Arquimea, etc. His current research interest is related to the development of electronics system for on-board satellites and space missions.
An Overview of the European HPC Strategy and Highlights from the Icelandic HPC Communities
The European HPC strategy aims to enhance Europe's competitiveness in the global HPC landscape by fostering collaboration, innovation, and providing access to state-of-the-art computing resources. As part of this strategy, the Icelandic National Competence Center (NCC) for HPC and AI has been established, serving as a hub for HPC expertise and research in Iceland. The Icelandic HPC communities have made remarkable progress in advancing research and development through high-performance computing applications. They have contributed significantly to various scientiﬁc domains, including remote sensing, computational fluid dynamics, and natural language processing among others, showcasing the breadth of their impact. Furthermore, the Icelandic HPC communities have actively participated in international partnerships, forging collaborations with European counterparts. These partnerships have facilitated the exchange of knowledge, sharing of resources, and promotion of cross-border scientiﬁc cooperation, reinforcing Iceland's role as a valued contributor to the European HPC ecosystem.
Hemanadhan Myneni, often referred to as Heman, is a theoretical physicist and computer scientist. He obtained his B.Sc. degree in Mathematics, Physics, and Chemistry from Andhra University, India, and an M.Sc. and Ph.D. dual degree in Physics from the Indian Institute of Technology Kanpur, India. Subsequently, Heman held various research positions at renowned institutions abroad, which allowed him to expand his research horizons. He contributed his expertise at institutions such as Université Grenoble-Alpes in France, the University of Delaware and Temple University in the United States, as well as the University of Iceland in Iceland. His research experience includes, but not limited to, the development and utilization of novel theoretical methods and scientific software codes for quantum-based materials modeling and atomistic simulations. Additionally, he focuses on gaining a comprehensive understanding of how these materials respond to external stimuli such as light, electric fields, and magnetic fields. Currently, Heman holds the position of Research Assistant Professor in the Computer Science Department at the University of Iceland. In addition, he is the head of the “Quantum Simulation and Data Science Lab” and is part of the National Comptenence Center for HPC and AI in Iceland. His lab aims to advance materials modelling and simulations by harnessing cutting-edge technologies in high-performance computing (HPC), data science (including artificial intelligence and machine learning), and quantum computing. Additionally, Heman is actively involved in the European Digital Innovation Hub in Iceland (EDIH-IS) to provide support to SMEs and public sector organizations in effectively leveraging the latest digital technologies.
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.