Applying Machine Learning to Earth Observations In A Standards Based Workflow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earth Observations (EO) enable scientific research, such as the study of meteorology and climate, ecosystems and forests, hydrology and marine life. Applications of EO help protect populations from disasters and improve life in intelligent cities. Increasingly, Machine Learning techniques are seen as key to solve these complex multidisciplinary problems. The scale and dimensionality of data involved often require the definition of processing chains, or workflows. Standards can facilitate the composition, sharing, execution and discovery of these workflows and applications, making them more useful. This paper presents three applications based on Deep Learning: a tree species classifier, a car detector and a flood detector. These applications rely on software containers to package ML framework and algorithms, as well as on workflows to process EO data. We found that these practices allow improved reuse and deployment of research assets in infrastructures. We also note the strong discriminative capabilities of Deep Learning on smaller datasets and the difficulty of gen-eralization to other methods of sensing or regions of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it