LAND COVER AND LAND USE CHANGE DETECTION OF BEIJING WITH TEXTURAL INFORMATION FROM SATELLITE REMOTE SENSING DATA
Bibliographic record
Abstract
Information about urban land cover and land use changes concerns many groups of people, such as the local governors, urban planners, environmentalists and decision makers. They need up-to-date change information to manage the land effectively and revise development plans. A case study was conducted over Beijing, China. Two multispectral satellite images (ASTER and SPOT PAN) collected in 2001 and a hyperspectral satellite image (CHRIS-PROBA) collected in 2005 were acquired over Beijing for the research. Both spectral and spatial information were considered in change detection. The result of the change detection indicates that the local government returned some of the lands back to forests and improved the environment in four years from 2001 to 2005. It also shows the increasing ability of Beijing in solving conflicts between urban growth and environment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".