COLLABORATIVE PLATFORM FOR PROTOTYPING, DEVELOPING AND EXECUTING EO AND AI BASED SERVICES
Bibliographic record
Abstract
Earth Observation (EO) data, coupled with advanced Artificial Intelligence (AI), offers unprecedented capabilities for monitoring human activity and the environment to support sustainable development. However, harnessing this potential requires addressing challenges in accessing and utilizing diverse data sources and platforms. Existing programs, such as Copernicus DIAS platforms, NextGEOSS, EOSC, and Copernicus RUS service, provide added-value EO services, but their differing data access policies and technical complexities hinder widespread use. In response, CS GROUP develops the METIS solution, which is a “Platform as a Service (PaaS)” data processing and analysis solution that is interoperable and based on open-source components. METIS solution offers a full-web collaborative Virtual Research Environment (VRE) dedicated to EO and geolocated data exploration & transformation. Users such as researchers, scientists and developers can leverage on the VRE to develop new processing chains, AI models and value-added applications easier and faster. To address satellite data access challenges, the EODAG, a STAC-compliant catalogue proxy, ensures transparent access to external catalogs and metadata. METIS serves as the foundation for operational systems, including the CALLISTO project, an EU H2020-funded initiative that leverages METIS for an interoperable Big Data platform. CALLISTO integrates cutting-edge technologies to analyze vast volumes of EO satellite data and heterogeneous sources, promoting accessibility and simplifying complex analyses within a secure and high-performance framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".