Discover the new approach to applications development with ‘Artificial Intelligence Knowledge Packs (AI KPs)’ in the GEO Knowledge Hub: Towards the Open and Reproducible Knowledge application for mine site monitoring
Bibliographic record
Abstract
To accomplish strategic objectives on zero-pollution, the entire mining life cycle (exploration, extraction, closure, mine- site rehabilitation) needs to develop minimal impact exploration and monitoring technologies and applications which are open to the broadest groups of stakeholders. In this respect Earth Observation (EO), Drone & Proximal Sensing and relevant in-situ data bring significant contribution for both, sustainable management of mineral resources and efficient multi-scale monitoring of mining impacts. In this sense, the purpose of the GEOMIN activity, part of the Group on Earth Observations (GEO) Work Programme [1] [2], is to increase awareness and use of state-of-the-art EO data and methods which represent a novel means for sustainable monitoring and management of mineral resources and efficient multi-scale monitoring mining impacts. How is GoldenEye project through AI-driven tools and applications enhances the GEOMIN community?In the scope of the GoldenEye project, OPT/NET delivered the next generation of AI exploitation system: a hybrid platform which combines the processing & automation capabilities of AI with the natural problem-solving abilities of humans. We have developed dedicated applications with our novel approach based on the Artificial Intelligence Knowledge Packs (AI KPs), integrated in GOLDENAI Engine, to rapidly interpret the geographical patterns and environmental impacts caused by the mining activity.What is the role of the GEO Knowledge Hub (GKH) as the Digital portal in promoting the replicability and re-usability of AI KP in the mining sector and how it relates to Goldeneye ?The GEO Knowledge Hub (GKH) is a central cloud-based digital library providing access to Earth Observations applications developed by GEO. The GEO Knowledge Hub is part of the GEOSS Infrastructure and helps the GEO to advance Open Knowledge. The scope of the GKH is to promote the replicability and re-usability of EO Applications by sharing with the end users, all the Knowledge Resources essential to fully understand and re-use them. All the Knowledge Resources are directly shared, curated and organized by the Knowledge Provider to ensure replicability with proper documentation.Therefore, several Knowledge Packages (KPs) related to the technological solutions of the GoldenEye project can be found in the GKH. In this paper, the KP related to the GOLDENAI platform will be presented, including the description of the integrated AIKPs, such as:AI KP for mineral mapping - Band-ratios based on WorldView-3 AI KP for mineral mapping - SPCA based on WorldView-3 AI KP for UML clustering based on Copernicus Satellite imagery (Sentinel-1 SLC) The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the GA 869398. References:[1] GEO (2023a). GEO Work Programme 2023-2025. Access 09 Jan. 2023. url: https://earthobservations.org/geo_wp_23_25.php [2] GEO (2023b). GEO WEEK 2022. Access 09 Jan. 2023. url: https://earthobservations.org
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".