Urbanisation viewed through a geostatistical lens applied to remote-sensing data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.003 |
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 it