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 a prior 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".