In the AI-Cube project datacube fusion and AI-based analytics will be integrated,
demonstrated in several real-life Earth Observation application scenarios,
and deployed on a federation of CODE-DE, DIASs and further high-volume EO/geo data offerings.
Goal is to support scenarios like the following: User selects a topic
(in the project: specific crop types, specific forest types, burnt forest areas).
System determines, through a combined analysis of various large-scale data sources,
a list of regions showing the criterion selected. User gets this visualized directly
or continues analysing, possibly combining with further data sources.
AI-Cube is in part funded by the German Federal Ministry of Economic Affairs and Climate Action (BMVK). Project runtime is from September 2021 through August 2023.
Large-Scale Scientific Information Systems Group, Prof. Peter Baumann (Coordinator)
Under the lead of Prof. Dr. Peter Baumann
the Large-Scale Scientific Information Systems
focuses on Array Databases, a research field we have pioneered since 1992:
flexible, scalable services on large, multi-dimensional arrays,
such as spatio-temporal sensor, image, simulation output, and statistics data - nowadays commonly also called "datacubes".
We address this field in all its aspects, such as algebraic modelling,
query languages, architectures, optimization, parallelization, distribution,
new hardware, application, and standardization.
Among the highlights of our research and standardization activities are
the free, open-source rasdaman array analytics server
the "Big Datacube" standards in OGC, ISO, and INSPIRE
This has been honoured by a series of innovation awards
Remote Sensing Image Analysis Group, Prof. Demir Begüm
The Remote Sensing Image Analysis Group
led by Prof. Begüm Demir, is part of the Faculty of EECS at TU Berlin.
Our group performs research in the field of processing and analysis of remote sensing images
for Earth observation with interdisciplinary approaches associated to remote sensing,
machine learning, signal and image processing and big data management.
Deep-tech SME rasdaman GmbH
is a German independent academic spin-off with mission to commercializing the rasdaman ("raster data manager") datacube technology.
Its game-changing Actionable Datacubes® paradigm stands out through its flexibility, scalability, security, open standards support (such as OGC and INSPIRE),
and planetary-scale federation capabilities on Big Earth Data. The rasdaman engine is mature (TRL 9) and proven on 130+ Petabyte datacube assets,
with queries parallelized more than 1,000x in AWS; it scales seamlessly from cloud to edge, such as nanosats.
Documented through publications and patents, the rasdaman team has pioneered Datacubes and Array Databases
and remains world technology leader in high-performance analytics and fusion on spatio-temporal datacubes.
Terms of Reference
Jacobs University Bremen gGmbH
c/o Peter Baumann
Campus Ring 12
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