Assessing the use of cross-orbit Sentinel-1 images in land cover classification
Why this work is in the frame
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Bibliographic record
Abstract
Land cover is the easiest detectable indicator of human intervention on land. Urban, peri-urban and agriculture areas present a complex combination of land cover, which makes classification challenging. Getting more detailed information is the aim of any classification method. In this study, improving land cover type classification using cross-orbit Sentinel-1 images is evaluated. To avoid uncertainties, three sites in different weather conditions and locations but approximately the same land cover is selected. For each study area, three datasets including polarimetric features extracted from (1) ascending orbit image, (2) descending orbit image, and (3) combined ascending and descending orbit images are produced and used for classification. Land cover classification is performed following a supervised Support Vector Machine (SVM) exploiting all three datasets. Consequently, the radar cross-orbit integrated dataset produced the most accurate land cover map. Classifications show overall accuracies (OA) of 84%, 85%, and 75%, and Kappa coefficients (K) of 0.67, 0.75, and 0.55 for Skane, Tehran, and Sherbrooke regions, respectively. Fortunately, the accuracy results are at least 4% better than single-orbit classification. In other words, the proposed mapping approach has proved that using information from both the ascending and descending dual-polarized images could achieve a more accurate classification map than using a single-orbit image individually.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it