Opening
Learning outcomes
• Learning outcomes
Welcome at the University of Santiago de Compostela and Opening of the School / IEEE GRSS and HDCRS activities
Welcome at the University of Santiago de Compostela and opening of the school .
The IEEE Geoscience and Remote Sensing Society (GRSS) focuses on advancing the science and technology of remote sensing and disseminating knowledge in this field. The society supports various technical committees and working groups that promote research, development, and education in geoscience and remote sensing. One such group is the “High-performance and Disruptive Computing in Remote Sensing” (HDCRS) of the GRSS Earth Science Informatics Technical Committee (ESI TC). HDCRS is the main organizer of this school, and its primary objective is to connect a community of interdisciplinary researchers in remote sensing who specialize in distributed computing (such as supercomputing and cloud computing), disruptive computing (e.g., quantum computing), and parallel programming models with specialized hardware (e.g., GPUs, FPGAs). The activities of HDCRS include educational events, special sessions, and tutorials at conferences, as well as publication activities, which will be presented
Instructor
Dora Blanco Heras
Biography
Dora B. Heras is a full 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
Biography
Gabriele Cavallaro (Senior 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 served as the deputy head of the "High Productivity Data Processing" (HPDP) research group at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Germany. Since 2022, he has been the Head of the "AI and ML for Remote Sensing" Simulation and Data Lab at JSC and an Adjunct Associate Professor at the School of Natural Sciences and Engineering, University of Iceland, Iceland. From 2020 to 2023, he held the position of Chair for the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group under the IEEE GRSS Earth Science Informatics Technical Committee (ESI TC). In 2023, he took on the role of Co-chair for the ESI TC. Concurrently, he serves as Visiting Professor at the Φ-Lab within the European Space Agency (ESA), where he contributes to the Quantum Computing for Earth Observation (QC4EO) initiative. Additionally, he has been serving as an Associate Editor for the IEEE Transactions on Image Processing (TIP) since October 2022.
CESGA: High Performance and Disruptive Computing
The contribution of the Galician Supercomputing Centre to the infrastructures and research on High Performance and Disruptive Computing will be presented
Instructor
Lois Orosa
Biography
Lois Orosa defended his PhD. in the University of Santiago de Compostela in 2013. He received the distinction of “Cum Laude” for the quality of his PhD thesis, and he has been doing research on Computer Architecture since then.
Lois Orosa is currently the Director of the Galicia Supercomputing Center (CESGA) since March 2022, which currently has 51 people. As part of the responsibilities assumed at CESGA, Lois Orosa is leading the Galicia Quantum Technology Hub (or “ Polo de Tecnoloxías Cuánticas de Galicia ”). The Hub strategy plan foresees an investment of 154M euros until 2030, from which around 30M were already executed.
Before joining CESGA, he was doing research at ETH Zürich for 4 years in the SAFARI Research group, lead by Onur Mutlu. Before that, he received a research grant to work on Computer Architecture for 3 years in the University of Campinas, where he co-advise a PhD student. He also has been doing other research stays on top International Institutions, both in Industry, in the companies IBM R&D (Haifa, Israel), Xilinx (Dublin, Ireland), Recore Systems (Enschede, Netherlands), and Academia, in Universidade de Illinois en Urbana-Champaign (USA), Universidade Nova de Lisboa (Portugal).
He has contributed very significantly to the field of Computer Architecture in the last few years, making very relevant contributions especially in reliability and security of computer systems. He published in the 4 top venues in this area in the last few years: 4 papers in ISCA, 5 papers in HPCA, 7 papers in MICRO, and 3 papers in ASPLOS, from which he received 19 HiPEAC awards , given to European researchers that publish in strong venues .
He also published 7 additional papers on top venues and journals (Q1 equivalent). The impact of these publications is significant in recent years (470 citations in the year 2023, 1512 citations in total). He also has presented several posters and short papers, and has given multiple talks about his research. He serviced the community by being a reviewer and a program committee member of many conferences, journals and workshops.
Edge computing for Earth Observation: Advances and Challenges in Artificial Intelligence (EO) on board satellites
After this lecture you will be able to:
- describe the concept of Edge computing and its application to space systems,
-decsribe the motivations and applications of Edge computing on Earth Observation satellites
- describe the building blocks that compose a Payload Data-Handling Unit, the main hardware solutions and their trade-offs
- discuss the main challenges in applying Artificial Intelligence (AI) on board satellites
- describe the current research trends related to onboard payload data-handling solutions, and novel mission concepts using Artificial Intelligence onboard satellites
The application of Artificial Intelligence (AI) algorithms for the processing of spaceborne data at “the Edge” on space systems could enable novel Earth Observation (EO) applications, novel mission paradigms featuring more responsive, reconfigurable and agile satellites. This lesson will first provide an insight on how AI algorithms could complement current EO missions and benefit EO applications. To this aim, it will provide basic knowledge on modern payload data handling units and main hardware solutions for processing AI algorithms on board space systems. Furthermore, it will highlight the main current challenges that limit the applicability of AI algorithms in space. Finally, it will provide an insight on current research trends and related space missions using Edge computing in space.
Instructor
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Gabriele Meoni
Biography
Gabriele Meoni received his MsC degree in Electronic Engineering and his PhD degree in Information Engineering from the University of Pisa respectively in 2016 and in 2020. After completing his doctoral studies, from September 2020 to April 2023 he held the position of Internal Research Fellow at the European Space Agency (ESA) (Advanced Concepts Team (ACT) September 2020 - August 2021, Φ-lab September 2021 - April 2023), where he conducted research on Artificial Intelligence (AI) and neuromorphic computing for onboard spacecraft applications. During 2022-2023, he was a visiting researcher at AI Sweden, focusing on distributed edge learning for satellite constellations. From May 2023 to April 2024, he served as an Assistant Professor in the Faculty of Aerospace Engineering at Delft University of Technology. Currently, Meoni is an Innovation Officer at ESA, with research interests spanning satellite onboard processing, AI for Earth Observation, and neuromorphic computing. Meoni coauthored more than 40 scientific publications.
Google Earth Engine for Earth Observation
Google Earth Engine (GEE) is a cloud-based platform that democratizes access to Google's computational capabilities, allowing for planetary-scale geospatial data analysis to address critical societal issues. Unlike other platforms, it is an integrated tool that is not only accessible to remote sensing scientists but also to a broader audience, who may lack the technical expertise to utilize traditional supercomputers or large-scale cloud computing resources. In this session, we will cover the basics of using GEE for Earth observation and data processing. You will learn how to work with GEE's multi-petabyte analysis-ready data catalog and understand the peculiarities and logic of using the platform properly. During the hands-on sessions, you will get practical experience in utilizing GEE's powerful machine-learning capabilities for classification and regression tasks. Additionally, we will show you how to import and export your own data and provide examples of how you can share your results with broader audiences using GEE's shareable user interfaces.
Instructor
Emma Izquierdo-Verdiguier
Biography
Emma Izquierdo-Verdiguier (Ph.D., 2014, University of Valencia) is an Assistant PostDoc in the Institute of Geomatics at the University of Natural Resources and Life Sciences (BOKU, Vienna). Dr. Izquierdo-Verdiguier is a Google Developer Expert from 2022, and her research interests are focused on the use of Earth Observation data and cloud computing environment for land surface phenology. Her previous research focused on machine learning applied to remote sensing data. In particular, nonlinear feature extraction based on kernel methods, and on automatic object identification and classification using multispectral images. She is also an external professor of the Master of Remote Sensing at the University of Valencia. In 2012, her paper was ranked second in the student’s competition of the IEEE Geoscience and Remote Sensing Symposium (IGARSS).