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.

The workplan is as follows:

• WP1: Project Management (ConstructorU)
• WP2: Infrastructure Setup (ConstructorU, TU-Berlin, rasdaman)
• WP3: ML Support in Array Databases (ConstructorU, rasdaman)
  • Task 3.1: Language Concepts for ML Queries in Array Databases (ConstructorU)
  • Task 3.2: Prototypical Implementation of CBIR Extensions in rasdaman (rasdaman)
  • Task 3.3: Server-Side Optimization of ML Queries (rasdaman)
• WP4: Multi/Cross-Modal Datacube-enabled AI Methods (TU-Berlin)
  • Task 4.1: Characterisation of large-scale multi-modal EO data (TU-Berlin)
  • Task 4.2: Multi-modal EO data retrieval (TU-Berlin)
  • Task 4.3: Cross-modal EO data retrieval (TU-Berlin)
• WP5: Evaluation (rasdaman, TU-Berlin)
  • Task 5.1: Technical Validation on a DIAS (rasdaman)
  • Task 5.2: Application Validation on DIAS (rasdaman, TU-Berlin)
• WP6: Outreach (rasdaman, ConstructorU, TU-Berlin)
  • Task 6.1: Web pages, project flyer (ConstructorU)
  • Task 6.2: Publications (ConstructorU, TU-Berlin)
  • Task 6.3: Continuous Media Presence (rasdaman)

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.

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

Demonstration

for $S1 in (S1_GRDH_IW),
    $S2 in (S2_L2A)
let $patch := [ {space-time selection of 256x256 patch} ],
return
    rsvqa.predict2(
        $S1[subs2], $S2[subs2],
        "rsvqa_trained_model.pt",
        "Are there some airports?"
    ) 

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.

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.

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.

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