The Potential of Radarsat Imagery for Population Estimation: Riyadh Case
Bibliographic record
Abstract
A spaceborne synthetic aperture radar (SAR) image acquired by the Canadian Radarsat-1 system and covering the city of Riyadh was used to investigate the potential of satellite radars for population estimation. Eight districts with population known apriori were utilized for the purpose. A PCI version 10 software was used to delineate district boundaries and to measure their areas in kilometers. A mathematical relationship was then established between area of district in square kilometers and population in thousands using information from four of these districts. The derived equation was then used to compute the population of the remaining districts. Errors in estimated population figures were then computed and averaged. The results showed that high resolution SAR imagery acquired from spaceborne platforms could be used to derive information about population to an accuracy of ±17%. Although this figure is large compared with those obtained from aerial photography (±6% – ±10%), it still points to the fact that when it is impossible or uneconomical to use aerial photography, radar imagery provides rough information about population. This is a worthwhile conclusion in circumstances such as relief operations and studying effects of natural disasters where preliminary information is urgently needed for the sake of taking further remedial actions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".