Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation
Bibliographic record
Abstract
There are increasingly more choices from a complex of data resources, classification algorithms, and methods of training sample selections. To increase the repeatability of digital classifications of remotely sensed data with consistently high accuracy, it is essential to use optimal classification options or factors. In this paper, two temporal sets of Landsat thematic mapper (TM) data, three classifiers and three approaches of training sample selections were tested for mapping deforestation. The use of these different factors can have significant effects on classification accuracy. The mixed effects of the three factors can also magnify the variations of classification accuracy. The use of bi-temporal data, a spatial‐spectral classifier, and hybrid training samples results in steadily higher classification accuracy than the combination of uni-temporal data, a spectral classifier, and image training samples. For the purpose of characterizing managed forest lands, even a small increase in overall accuracy of image classification is important because it may represent a large decrease in the variations of the producer's and user's accuracy, which in turn can reduce the uncertainties of area measurements for forest coverage.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".