Datacubes scale. AI understands.

About AI-Cube

In the AI-Cube project datacube fusion and AI-based analytics have been 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 was 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 was in part funded by the German Federal Ministry of Economic Affairs and Climate Action (BMVK). Project runtime was from September 2021 through August 2023.


ML in the Datacube Engine

The rasdaman datacube engine has been enhanced so that ML models can be invoked from within datacube queries in the OGC-standardized WCPS geo datacube query language via User-Defined Functions (UDFs) to invoke pytorch in the server for model application. From a user (i.e., query writer) perspective this external code appears like a regular query function. It can be used, but not downloaded so that Intellectual Property remains protected. The following example illustrates the principle how pretrained ML models, stored in the database, can be invoked as part of a general analytics query:

for in (Sentinel_2a),
    $m in (CropModel)
return encode( nn.predict( $c[...], $m ), "tiff" )

The most important advantages of this integration is that now ML is seamlessly integrated into EO datacube analytics. For example, data can be extracted from a datacube, preprocessed as necessary, then the ML model can work on it, and the output can be processed further with all image processing and statistics functionality available. Additionally, there is a full integration into all optimization, distributed processing, etc. the datacube engine provides already.

A particular twist of the TU Berlin contributed RSVQA technique is the integration with natural language processing: A question is submitted along with the Sentinel patches and the model, and the output again is natural language. The WCPS query has such a structure:

for $S1 in (S1_GRDH_IW),
    $S2 in (S2_L2A)
let $patch := [ {space-time selection of 256x256 patch} ],
        $S1[subs2], $S2[subs2],
        "Are there some airports?"

Further, the TU Berlin Begum Demir team has developed ConfigILM, a pytorch based library that enables fast development of visual question answering systems. Visual Question Answering (VQA) in remote sensing refers to the task of answering a question formulated in natural language related to the semantic content of the remote sensing images. To support development of models for this task, the Remote Sensing Image Analysis Group (RSiM) at TU Berlin created ConfigILM, a pytorch-based library for configurable combination of pre-trained image and language models.


AI-Cube Partners

Large-Scale Scientific Information Systems Group, Prof. Peter Baumann (Coordinator)

Under the lead of Prof. Dr. Peter Baumann, the Large-Scale Scientific Information Systems Group 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 and 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 (RSIM), 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.

rasdaman GmbH

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

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