Assessment of the benefit of a single sentinel-2 satellite image to small crop parcels mapping
Bibliographic record
Abstract
The present study demonstrates the feasibility of producing crop type maps using a single high spatial resolution image from Sentinel-2A (S-2A). Our main objectives focus on the evaluation of the potential of S-2A data for crop mapping in the Tadla Plain and the determination of the most suitable period for discriminating different crops using a single S-2A image. In order to achieve these objectives, we have acquired a set of seven images of the S-2A and twelve vegetation indices. Then, we produced crop type maps at a spatial resolution of 10 m using (SVM) and (RF) classifiers for each image. The accuracy and performance of classification were assessed using field data. The optimal period for mapping was done during March with an overall accuracy and kappa coefficient of 91.49%, 0.90 respectively. The results show that a single S-2A image should be sufficient for the discrimination of different crops.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".