A Comparative Study of Land Cover Mapping Based on Support Vector Machine
Why this work is in the frame
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Bibliographic record
Abstract
The richness of remote sensing images in information, due to the number of bands, makes them widely used in detecting and classifying terrestrial objects.The purpose of this study is a classification of multispectral images for mapping land occupation in the Mohammadia region (located in the west of Algeria).We developed a comparative study on the classification of the study subject image using the three following kernel functions: linear (LN), polynomial (PL), and radial basis function (RBF).After selecting the desired bands of the multispectral image, the training of the SVM is then carried out on the seven interest zones of the studied region: buildings, dense vegetation, sparse vegetation, forest, bare land, and roads.The obtained results are very promising, where the best classification rates were obtained by the use of the RBF kernel (97.91%) and the polynomial kernel (98.79%).
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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 | 0.000 |
| 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) | 0.000 | 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