About the Project
Project brief:
- Start: 01 Mar 2025
- Duration: 24 months
- Find us: fairgeo.org
- Funding information: Supported by EFRE Bremen
The Large-Scale Scientific Information Systems research group at Constructor University has worldwide reputation as pioneer of datacube services and Array Databases. The group is actively shaping OGC, ISO, and INSPIRE datacube standards.
rasdaman GmbH is the technology leader in high-performance Array Databases for flexible, scalable datacube services and, with Constructor University, shaper of the datacube standards. The proprietary rasdaman enterprise offers a unique combination of flexibility, scalability, federation, interoperability, green computing, and security.
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Research
Problem Statement
AI on geo data is receiving high attention currently. However, literature that AI models are primarily reliable on their training data (e.g., 85%) and drastically unreliable outside it (e.g., 20%). The illustration shows a field fruit classification in the Netherlands, applying a model produced by Wageningen Research. It is clearly visible how the model hallucinates over water (bottom left), providing incorrect results.
In this case, a geographical restriction (e.g., based on cadastral data) would already be sufficient to constrain the model to valid situations. However, in general the challenge can be of arbitrary complexity and often not decidable at all.
Currently, (i) there is no general method for characterizing the validity of models in terms of spatial, temporal, and content-based criteria; (ii) there is no method for using a validity characterization to protect specific user requests; and (iii) there is no method to automate these mechanisms within a general spatiotemporal geodata infrastructure. Published models – for example on HuggingFace with currently 827,859 models – at best have a textual description of their applicability, often overly optimistic and generic, and currently not machine-readable.
We summarize the capability of a server to recognize invalid data/model combinations and react appropriately as "model fencing" to express that AI models during their inference get constrained to their individual application conditions ("comfort zone"). FAIRgeo attempts to structure the field and find decision criteria for selected situations.
PS: we see the FAIRgeo interpretation of "FAIR" as compatible with the widely used expansion as "Findable, accessible, interoperable, reusable" - it is all about more usability and trust of (big) data.
News
Datacubes
Huh? Datacubes?
If you think about our atmosphere, do you think about zillions of NetCDF files? Probably not. You rather think of some 3D x/y/z space that changes over time - the fourth dimension, actually. Datacubes are intuitive and natural, simplifying wrangling of spatio-temporal data.
Multi-dimensional by Nature
Datacubes are not only 3D. They unify 1D sensor data, 2D imagery, 3D x/y/t image timeseries and x/y/z geological voxel data, 4D x/y/z/t atmospheric and ocean data, etc. Powerful, flexible standardized APIs as issued by OGC and ISO unify across all dimensions, simplifying access further.
Location-Transparent Federation
...establishes a single common information space where heterogeneous data sources feed homogenized datacubes for a flexible mix & match.
Curious? Try it Out!
Experience the award-winning, world-leading rasdaman datacubes hands-on with the Earth Datacube Playground!
Terms of Reference
Responsible Maintainer
|
Constructor University Bremen gGmbH c/o Prof. Dr. Peter Baumann Campus Ring 12 28759 Bremen Germany |
Contact: Peter Baumann,
Image Credits
Images credits: Shutterstock, Constructor University.
No Operational Use
This service is for technology demonstration purposes only. No responsibility whatsoever is taken on availability of the service, availability of data at some particular location and time, and correctness of the data. It is explicitly forbidden to base decisions, such as choice of flight path, on this demonstration service.
External Links
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