Using Satellite Images to Detect Land-Use Change in Al-'Ain City, United Arab Emirates
Bibliographic record
Abstract
Remote sensing data can be effectively used as an important tool to evaluate and monitor land-use and land-cover changes. Global coverage, high spatial resolution, and the revisit capabilities of modern remote sensing satellites provide us with a large amount of valuable data for accurate land-use estimation. In this study, land-use change detection for Al-'Ain city, United Arab Emirates (UAE), within approximately a ten-year period, was conducted. Three Landsat images of different dates (1984, 1989, 1993) were processed and analyzed, geometrically corrected (registered), and digitized to extract detailed land-cover information. Based on the combined use of multi-temporal satellite imagery and ancillary data, such as topographic maps, aerial photographs, and field check (ground truth) data, land-use maps with six different classes were prepared, showing the substantial rate of change and the usefulness of Landsat data in detailed mapping and in land-use change detection studies.
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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 | 0.002 | 0.001 |
| 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 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".