Session 1 - Data analytics and AI
Keynote Speaker: Xiaoxiang Zhu (DLR)
Title: Artificial Intelligence and Data Science in Earth Observation
Abstract: Geoinformation derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Geoscience, atmospheric sciences, cartography, resource management, civil security, disaster relief, as well as planning and decision support are just a few examples. Furthermore, Earth observation has irreversibly arrived in the Big Data era, e.g. with ESA’s Sentinel satellites and with the blooming of NewSpace companies. This requires not only new technological approaches to manage and process large amounts of data, but also new analysis methods. Here, methods of data science and artificial intelligence (AI), such as machine learning, become indispensable.
In this keynote, explorative signal processing and machine learning algorithms, such as compressive sensing and deep learning, will be shown to significantly improve information retrieval from remote sensing data, and consequently lead to breakthroughs in geoscientific and environmental research. In particular, by the fusion of petabytes of EO data from satellite to social media, fermented with tailored and sophisticated data science algorithms, it is now possible to tackle unprecedented, large-scale, influential challenges, such as the mapping of global urbanization — one of the most important megatrends of global changes.
Bio: Xiaoxiang is since 2018 the Head of the EO Data Science Department at DLR-IMF, and Head of Team "Big Data Analytics". Her main research Interests are on modern signal processing for Earth Observation, advanced InSAR techniques, optical and hyperspectral remote sensing, computer vision in remote sensing and Machine Learning
http://www.sipeo.bgu.tum.de/team/zhu
Session 2 - Interactive processing and visualisation
Keynote Speaker: Thomas Huang (NASA JPL)
Title: Overview of JPL data science for Earth science
Abstract: As Alvin Toffler had eloquently put it “You’ve got to think about big things while you’re doing small things, so that all the small things go in the right direction.” JPL has a long history of building many innovative solutions for onboard instrument, ground operation and data system, archive and distribution for our missions. As the rate of data generate from our missions continue to increase and is expected to rise significantly in near future, JPL is engaging data science and artificial intelligence technologies and methodologies for mission operations and to enable science. A program is established to focus on implementing an institution-wide strategy for data science and artificial intelligence, which include expanding from archives to enable data analytics, methodology transfer across disciplines, and establish research partnerships with academia, government, and industry. In recent years, JPL made significant advancement to improve Earth science through machine learning, intelligent search, data fusion, interactive visualization and analytics.
Bio: Thomas Huang is a Technical Group Supervisor for the JPL’s Computer Science for Data-Intensive Applications group. He is also the Strategic Lead for Interactive Analytics for the National Space Technology Applications Program Office, the Principal Investigator on several NASA Cloud-based big data analytic projects, and the System Architect for the NASA’s Sea Level Change Portal. As an expert in large-scale, distributed intelligent data systems, Thomas led both planetary and earth data system projects. Thomas was the Project Technologist for the NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC). As an advocate for free and open source software, Thomas led the open sourcing of many NASA-funded technologies. He recently established the Apache Science Data Analytics Platform (SDAP) as a community-driven, Cloud-based Analytic Center Framework. Thomas is a Computer Science lecturer at the California State Polytechnic University, Pomona, and member of its Industry Advisory Board..
Session 3 - Data discovery and access
Keynote Speaker: Volker Markl (TU Berlin)
Title: Mosaics in Big Data: Stratosphere, Apache Flink, and Beyond
Abstract: The global database research community has greatly impacted the functionality and performance of data storage and processing systems along the dimensions that define “big data”, i.e., volume, velocity, variety, and veracity. Among our contributions are: (1) establishing a vision for a database-inspired big data analytics system, which unifies the best of database and distributed systems technologies, and augments it with concepts drawn from compilers (e.g., iterations) and data stream processing; (2) forming a community of researchers and institutions to create the Stratosphere platform to realize our vision. One major result from these activities was Apache Flink, an open-source big data analytics platform and its thriving global community of developers and production users. Although much progress has been made, when looking at the overall big data stack, a major challenge for database research community still remains. That is, how to maintain the ease-of-use despite the increasing heterogeneity and complexity of data analytics, involving specialized engines for various aspects of an end-to-end data analytics pipeline, including, among others, graph-based, linear algebra-based, and relational-based algorithms, and the underlying, increasingly heterogeneous hardware and computing infrastructure. At TU Berlin, DFKI, and the Berlin Big Data Center (BBDC), we aim to advance research in this field via the Mosaics project. Our goal is to remedy some of the heterogeneity challenges that hamper developer productivity and limit the use of data science technologies to just the privileged few, who are coveted experts.
Bio: Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin) and was an Adjunct Full Professor at the University of Toronto until June 2018. At the German Research Center for Artificial Intelligence (DFKI), he is both a Chief Scientist and Head of the Intelligent Analytics for Massive Data Research Group. In addition, he is Director of the Berlin Big Data Center (BBDC) and Co-Director of the Berlin Machine Learning Center (BzMl). Earlier in his career, he was a Research Staff Member and Project Leader at the IBM Almaden Research Center in San Jose, California, USA and a Research Group Leader at FORWISS, the Bavarian Research Center for Knowledge-based Systems located in Munich, Germany. Dr. Markl has published numerous research papers on indexing, query optimization, lightweight information integration, and scalable data processing. He holds 20 patents, has transferred technology into several commercial products, and advises several companies and startups. He has been both the Speaker and Principal Investigator for the Stratosphere Project, which resulted in a Humboldt Innovation Award as well as Apache Flink, the open-source big data analytics system. He serves as the President-Elect of the VLDB Endowment and was elected as one of Germany's leading Digital Minds (Digitale Köpfe) by the German Informatics (GI) Society. Most recently, Volker and his team earned an ACM SIGMOD Research Highlight Award 2016 for their work on “Implicit Parallelism Through Deep Language Embedding.” Volker Markl and his team earned an ACM SIGMOD Research Highlight Award 2016 for their work on implicit parallelism through deep language embedding.
Session 4 - New Challenges for Big Data
Keynote Speaker: Harald Hauschildt (ESA)
Title: European Data Relay System Achievements and Capabilities
Abstract: With the first European Data Relay Satellite (EDRS-A) launched on the 29th of January 2016, the ’Space Data Highway’, EDRS is revolutionising satellite communications as Europe’s first optical space communication network, capable of relaying user data in near-real time at an unprecedented 1.8 Gbit/s. Its extension programme, called EDRS Global will enlarge the network to the Asia-Pacific Region, providing Quasi-real-time services on a global scale. Via EDRS and EDRS Global data can be picked up from LEO satellites and transferred via Laser Link even between the EDRS geostationary satellites and delivered into European territory without the need for any additional ground station. In this context, EDRS and EDRS Global and its laser communication technology represents a strategic element for Europe to boost European technology adoption. This has become one element of a successful European implementation of the Big Data economy. EDRS and EDRS Global as a multi-gigabit and global communications system and service supports the data infrastructure, similar to what GEANT is for interconnecting European research networks. EDRS Global could be an element for interconnecting relevant European space resources, enhancing the provision and availability of data to the broad user community. By today more than 16000 laser links have been performed making EDRS and EDRS Global an important component of the Big Data from Space generated by the Sentinel satellites operated by ESA in the framework of the Copernicus programme funded and managed by the European Commission.
Bio: Dr. Harald Hauschildt is the ESA Program Manager for the newly created ScyLight Program, dedicated to Optical Communication and Space based Quantum Cryptography. In this role Harald is also in charge for the preparation of ESAs HydRON programme to develop and demonstrate a “High Throughput Optical Network” in Space. Furthermore Harald is preparing the extension of the European Data Relay System (EDRS) to provide global coverage. In his former career life Harald worked at DLR (German Aerospace Centre) in the role of the German Delegate to ESA and was responsible for the German participation in the USA-German Laser Crosslink Program between the US NFIRE and the German TerraSar-X Satellite which was the starting point for ESAs EDRS Programme.
Dr. Hauschildt has a PHD in Physics and Astro-Physics from the University Bonn, Germany and has been working at the California Institute of Technology (Caltech), Pasadena (USA) and the Max-Planck-Institute for Radioastronomy, Bonn (Germany)..
Session 7 - The Time Dimension
Keynote Speaker: Gustau Camps-Valls (Universitat de València)
Title: Machine learning in Earth Observation data analysis
Abstract: In this talk, several approaches developed in the last years to tackle specific problems in remote sensing data analysis will be presented, in particular upon developments on 1) image classification by exploiting the spatial and temporal data structures with deep recurrent networks, 2) bio-geo-physical parameter retrieval with advanced Gaussian processes that can predict several variables simultaneously and fuse heterogeneous multisensory data while providing credible confidence intervals for the estimates, 3) nonlinear methods to decompose Earth data cubes in spatially-explicit and temporally-resolved modes of variability that summarize the information content of the data and allow for identifying relations with physical processes, 4) new machine learning models that allow for optimal and online climate models ensemble learning, and 5) exploitation of Google Earth Engine to develop data assimilation to fuse multiresolution data and estimate/spatialize variables.
Bio: Gustau Camps-Valls (IEEE Member'04, IEEE Senior Member'07) received a B.Sc. degree in Physics (1996), in Electronics Engineering (1998), and a Ph.D. degree in Physics (2002) all from the Universitat de València. He is currently a Full professor in Electrical Engineering and Head of the Image and Signal processing (ISP) group, http://isp.uv.es. He is interested in the development of machine learning algorithms for geoscience and remote sensing data analysis. He is an author of around 180 journal papers, more than 200 conference papers, 20 international book chapters, and editor of the books "Kernel methods in bioengineering, signal and image processing" (IGI, 2007), "Kernel methods for remote sensing data analysis" (Wiley & Sons, 2009), "Remote Sensing Image Processing" (MC, 2011) and "Digital Signal Processing with Kernel Methods" (Wiley & sons, 2017). He holds a Hirsch's index h=60, entered the ISI list of Highly Cited Researchers in 2011, and Thomson Reuters ScienceWatch identified one of his papers on kernel-based analysis of hyperspectral images as a Fast Moving Front research. In 2015, he obtained the prestigious European Research Council (ERC) consolidator grant on Statistical learning for Earth observation data analysis. He is a referee and Program Committee member of many international journals, conferences, and technical committees. In 2016 Prof. Camps-Valls was included in the prestigious IEEE Distinguished Lecturer program of the GRSS, and in 2018 he was elevated to IEEE Fellow member.
