MONITORING RAPID URBAN EXPANSION: A CASE STUDY OF CALGARY
Bibliographic record
Abstract
that the city has close to one million inhabitants. There are many challenges that face rapidly expanding urban areas including appropriate urban planning and the building and maintenance of critical infrastructure. Remotely sensed data have proven useful for monitoring urban development since the launch of the Landsat-1 satellite in 1972. From 1999 to 2003, the Landsat-7 satellite provided high-quality, low-cost data that can be enhanced through data fusion processes to provide 15 metre spatial resolution multispectral data. In this research, fused Landsat-7 image data for the years 1999, 2000, 2001, and 2002 were analyzed to determine the rate of urban expansion. Each image was acquired at approximately the same time of year (July-August) to minimize the effects of varying sun positions. Texture measures proved valuable in accounting for and distinguishing varying degrees of “greenness ” in the imagery. They were also useful in separating agricultural fields from urban features. A combined unsupervised classification/image differencing change detection process with a combination of inputs including image texture, principal components, and the Normalized Difference Vegetation Index (NDVI) allowed for the monitoring urban development. The average expansion was estimated to be 4.62 square kilometres per year for the 1999-2002 period. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".