The Applications of High-resolution Satellite Images for Land Cover monitoring in Nanji Islands
Bibliographic record
Abstract
This paper describes the monitoring of land covers using remote sensing technique. IKONOS imagery which is the world's first 1-meter commercialized remote sensing satellite imagery is used to obtain the land cover and its dynamic information. The methods for geometrical correction method, image fusion method and supervised classification method are discussed. The information of the land cover types of Nanji Islands is derived from these images using supervised classification method, threshold method, vegetation index method and man-machine exchange method. The results show that the natural land cover types such as meadows and shrubbery are the main types which occupy about 70% area of the Islands, that the accuracy of the supervised classification method is about 76.31% and the Kappa coefficient about 0.71. Paying the especial attention to the main island, its area and shoreline derived from IKONOS images are 752 sq. km and 34.64 km, respectively. Comparing the statistics values, their biases are 1.46% and 456%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".