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 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 | 0.001 | 0.002 |
| 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".