A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada
Bibliographic record
Abstract
Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification model accuracy using a bootstrap approach. Using this method, it was found that classification accuracy and confidence, while closely related, can be used in complementary ways to provide additional information on map accuracy and define groups of classes and to inform the future reference sampling strategies. Overall classification accuracy increases with an increase in the number of fields surveyed, where the width of classification confidence bounds decreases. Individual class accuracies and confidence were non-linearly related to the number of fields surveyed. Results indicate that some classes can be estimated accurately and confidently with fewer numbers of samples, whereas others require larger reference data sets to achieve satisfactory results. This approach is an improvement over other approaches for estimating class accuracy and confidence as it uses repetitive sampling to produce a more realistic estimate of the range in classification accuracy and confidence that can be obtained with different reference data inputs.
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".