Land-cover mapping in an agriculture zone using simulated Sentinel-2 data
Bibliographic record
Abstract
Remote sensing technologies are used to assist in the mapping and monitoring of land cover in space and time. The European Space Agency’s (ESA) upcoming Sentinel-2 MultiSpectral Instrument (MSI) to be launched in 2013 has improved spatial and spectral properties compared to the current large-swath medium-resolution satellite sensors. Prior to the deployment of future sensors it is important to simulate and test the sensor data to evaluate the sensor's potential performance in producing the existing data products and develop new algorithms. This study simulated Sentinel-2 MSI data from airborne hyperspectral data over an agriculture area in northern Alberta, Canada. The standard Sentinel-2 MSI land-cover product was evaluated by comparing it to one created from the standard Landsat 5 TM and SPOT 5 HRV data products. Furthermore the standard Sentinel-2 MSI water column content band configuration and algorithm was evaluated for atmospheric correction purposes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".