• Learning outcomes
Prof. 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.
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).
Prof. 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.
Dr. 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 head of the ‘‘AI and ML for Remote Sensing’’ Simulation and Data Lab 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.
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.
Dr. Bertrand Le Saux
Bertrand Le Saux (Ms. Eng. 1999, MSc. 1999 INP Grenoble, PhD 2003 Univ. Versailles / Inria, Dr. Habil. 2019 Univ. Paris-Saclay) is a scientist with the European Space Agency ESRIN/Phi-lab, in Frascati (IT). He is working on data-driven techniques for visual understanding, with a background in machine learning and computer vision. He's interested in tackling practical problems that arise in Earth observation, to bring solutions to current environmental and societal challenges.
He has been a researcher at CNR/ISTI Pisa (IT), Univ. of Bern (CH), ENS Cachan (FR) and ONERA (FR). He was co-chair [2015-2017] and chair [2017-2019] for the IEEE GRSS technical committee on image analysis and data fusion (IADF TC). He is currently an associate editor of the Geoscience and Remote Sensing Letters. He is also a co-organiser of the CVPR / EarthVision workshop series.
ESA’s Quantum Computing for Earth Observation (QC4EO) Initiative: Current Activities and Perspectives
The AI-enhanced Quantum Initiative for EO is a recent initiative from ESA Earth Observation Programmes to assess the potential of Quantum Computing for Earth Observation (QC4EO). Indeed, quantum computing has the potential to improve performance, decrease computational costs and solve previously intractable problems in EO by exploiting quantum phenomena. In this talk we will present our current activities for discovering possible synergies between QC and EO, exploring first promising use-cases, and gathering both communities to prepare the ground for the opportunities which will arise with quantum computing developments.
Dr. Claudia Vitolo
Claudia is a senior scientist for the European Space Agency Centre for Earth Observations, where she works on Digital Twin Earth Applications. She previously worked as scientist for Diagnostic and Environmental Applications teams at the European Centre for Medium-range Weather Forecasts and as researcher in various academic institutions. Claudia's expertise is in geospatial data analysis, web services and artificial intelligence applied to Earth Observations, weather-driven disaster forecasting and other environmental applications.
Claudia lectured in Geographic Information Systems and Data Analysis, maintains a number of open-source projects and she is also an associate editor for the Geoscience Data Journal of the Royal Meteorological Society (Wiley). She co-founded two organisations: the R-Ladies Global and WomenInGeospatial+. Claudia served as voting member of the R-Consortium Infrastructure Steering Committee and was awarded the Microsoft Most Valuable Professional Award.
Introduction to the Destination Earth project
How can we better understand our planet’s past, act on the present issues and predict its future challenges? What if we could generate a digital replica of the Earth as a whole and unveil its inner functioning? This is not a science fiction, but what ESA is currently working on for the Destination Earth project in collaboration with scientists from ECMWF and EUMETSAT: a Digital Twin of the Earth. This talk will present an overview of the Destination Earth project, various related activities as well as the vision for moving forward within this challenging and ambitious endeavor.
Tom Augspurger is a software engineer at Microsoft where he works on the Planetary Computer. He contributes to several open-source libraries, including pandas and Dask.
Geospatial Data Analysis with the Microsoft Planetary Computer
This lecture will cover geospatial data analysis with the Planetary Computer. We’ll see how the Planetary Computer’s STAC API enables searching a large data catalog to find just the items of interest. We’ll use the Planetary Computer Hub to access data directly from Azure Blob Storage using compute in the same region. We’ll parallelize our computation onto a cluster of machines using Dask and Dask Gateway.
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