The IEEE Geoscience and Remote Sensing Society (GRSS) is developing the next-generation EO infrastructure for the archival, curation, and sharing of open data and computational resources - for the membership, by the membership. This task responds to the growing need for infrastructure support within the GRSS community, including TCs, Chapters, Global Activities, Industry. Further, it is a response to GRSS strategic direction SD1 EO Data and Compute Resources for Technical Development. Stakeholders of this infrastructure activity include GRSS (to address SD1, increase membership, contribute to outreach); TCs (common platform to leverage); GRSS members (centralized resources - links to publications, data, platform, code); general public (incentive to join, centralized repo and platform), Chapters (platform for sharing/using datasets and training). To start with, as agreed in the December 2021 AdCom, in 2022 existing cloud platforms and services should be explored and studied, among others.
The EO-Cube project picks up on this request for action with a specific contribution for evaluating and demonstrating the value of datacube management and analytics as a component of the GRSS EO infrastructure. Datacubes provide a unified view on n-D raster data, such as 1-D sensor data, 2-D imagery, 3-D x/y/t image timeseries and x/y/z geophysical data, 4-D x/y/z/t atmospheric data, etc. – moving from a sensor-centric to a user-centric organization of data. Datacubes are an accepted corner-stone of analysis-ready data and well understood meantime, with established data and service standards in OGC, ISO, and EU INSPIRE; in fact, IEEE GRSS has played an important role in this standardization work.
EO-Cube is a Strategic Initiative of IEEE GRSS implemented by the Earth Science Informatics (ESI) Technical Committee, supported by IEEE GRSS Strategic Initiative funds which are gratefully acknowledged.
Following a staged approach, EO-Cube starts with a prototype service of a fully standards-based open GRSS EO datacube infrastructure, for seamless embedding into the overall GRSS infrastructure.
It makes use of state of the art array database technology with datacube query support, federation c apabilities, and appropriate access control. A federation with existing Petascale EO datacube assets will enable completely location-transparent distribu ted datacube fusion. Further, current technology trends - such as from ISO and OGC with the forthcoming ARD and Datacube WGs - will be duly considered. By the end of the activity the service will be ready for seamless transition to sustained operation, initially operated in the AWS cloud, it can be moved or multiplied to any common cloud; the federation can also span heterogeneous clouds and heterogeneous hardware - tested from supercomputers to laptops to nanosats.
In this federation, any query can be sent to any node, regardless which member node holds the data - this includes distributed data fusion.
The WCPS query on the left side determines global heavy rainfall risk areas
by combining precipitation and Landsat8 data stored on different servers each.
run query the WCPS request gets submitted to the server chosen
- either CODE-DE, the German Copernicus hub, or Cloudferro / Creodias.
On the map the query path is drawn: from your location to the server picked, and onward a subquery automatically sent to the other server.
The result is shown in NASA WorldWind - it is both time the same, illustrating the principle of location-transparent distributed data fusion.
Peter Baumann (chair, IEEE GRSS Earth Science Informatics TC) leads the Large-Scale Scientific Information Systems Research Group at Constructor University. Our research has pioneered Array Databases, aiming at flexible, scalable services on large, multi-dimensional arrays - commonly called datacubes - such as spatio-temporal sensor, image, simulation output, and statistics data.
That Dai Hai Ton -- chair, IEEE GRSS Databases in Remote Sensing Working Group. He is a research scientist at The Earth System Science Center (ESSC) in The University of Alabama in Huntsville (UAH) and adjunct faculty of the Jarvis College of Computing and Digital Media (CDM) at DePaul University at DePaul University. His current research interests focus on the application of novel big data, data computational and spatio-temporal database approaches to the storage, format, processing, discovery, analysis and visualization of Earth Observation Data.
The service is under construction - and we are grateful for your input:
If any of these apply, or you want to let us know of anything else: Contact the initiative lead, !
With EO-Cube, currently in prototype stage, the IEEE GRSS Earth Science Informatics Technical Committee (ESI TC) is contributing a user and programming friendly, powerful, standards-based service constituting the next-generation EO infrastructure - for the membership, by the membership.
Constructor University Bremen gGmbH
c/o Peter Baumann
Campus Ring 12
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