Accuracy assessment of annual land cover time series derived from change-based updating
Bibliographic record
Abstract
The development of temporally consistent land cover time series from satellite-based earth observation has proven difficult due to variability in sensor observations. This leads to spurious land cover differences between maps when standard supervised classification approaches are applied. To reduce this effect, a common solution has been to first detect change and update a base map for only these change areas. Assessing the accuracy of land cover time series is challenging because multiple maps need to be assessed for both land cover classification and change detection accuracies. Regarding a change based updating approach; accuracy is close to that of the original base map for a time series where only a small percent change occurs. Over longer periods where significant change has accumulated the accuracy becomes more dependent on the change and update labeling accuracy. Thus, accuracy for a change based approach can be seen as a function of the base map, change detection, and update accuracies. A specific formulization is developed to summarize these components and applied to investigate accuracy of a 250 m resolution time series for Canada. Results show that the time series accuracy was in a large degree predetermined by the base map accuracy because there was only a small amount of change over the period and the base map and update accuracies were similar. Increasing the update accuracy by a few percent, within the precision of its estimation, would improve the accuracy of the time series evaluated as it is extend in time.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".