Urbanisation viewed through a geostatistical lens applied to remote-sensing data
Bibliographic record
Abstract
The purpose of this study is to investigate the usefulness of variography for landscape change detection when applied to a time series of unclassified remote-sensing data. Specifically, the challenge was to identify and describe land-cover change, the result of rapid urbanisation, across a 12-year chronology of satellite images for which little temporally specific ground information was available. Using semivariograms, and the remote sensing technique of band-overlay for visual reference, the change in spatial extent of land-cover type, as well as feature richness (variance in reflectance values), was determined for Landsat and SPOT imagery obtained for the Sanya Region of Hainan, China in 1987, 1991, 1997 and 1999. Comparison of results with a traditional post-classification change trajectory confirms that time-series semivariograms are instructive at identifying general changes to land cover resulting from urbanisation. They are complementary of traditional post-classification approaches where sufficient in-situ and time-specific data exist; where these data are absent, the semivariogram approach to change analysis is recommended as a precursory tool for monitoring land-cover change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".