Derivation of Land Cover Continuous Fields over Canada from SPOT-VGT Imagery
Bibliographic record
Abstract
Recent comparisons of coarse (1 km) and fine (30m) resolution land cover maps across Canada indicate that single cover types rarely occupy more than 40% of a 1km pixel in forested areas. To address this aggregation problem we develop and apply a method for estimating continuous fields of vegetation structural characteristics using 1km resolution SPOT-VEGETATION (VGT) imagery. A sample of Landsat TM and ETM+ scenes stratified by ecozone is classified using a standard methodology to generate spatially distributed calibration centres. Neural networks, look-up-table labelling, and regression resulted in biases as large as 35% when calibrated using centers over 400km away. Only the linear least squares inversion approach produce a bias under 20%. An estimator based on a linear mixture model regularised by the <I>a prior</I> continuous field distribution over the calibration centres is developed. The regularisation parameter is defined by the spectral and spatial similarity of VGT reflectances between calibration centres and the regions being mapped. A strategy for mapping and validating Canada wide continuous fields using this method is described.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".