Developing a Semi-Supervised Strategy in Time Series Mapping of Wetland Covers: A Case Study of Zrebar Wetland, Iran
Bibliographic record
Abstract
Abstract Wetlands, essential for Earth’s health, ecological balance, and local economies, require accurate monitoring and assessment for effective conservation. Data-driven models based on remote sensing are highly capable of monitoring the status and classification of wetlands. This study developed a semi-supervised framework for mapping wetland covers in Zrebar, Iran, using Landsat time series data from 1984 to 2022. A pixel purification technique was applied to the temporal candidate images to refine the initial training data (conventional scenario) and generate purified training data (proposed scenario). The Support Vector Machine (SVM) algorithm was utilized to classify the land cover within the wetland, and the accuracy of the two scenarios was evaluated and compared. Over the study period, the analysis of land cover changes within Zrebar Wetland revealed significant spatial and temporal changes in soil and farmland, reed, and water from 1984 to 2022. The omission error rates for the classes soil and farmland, reed, and water were decreased from 0.14, 0.14, and 0.12 for scenario 1 to 0.03, 0.05, and 0.05 for scenario 2, respectively. In addition, the commission error for these classes decreased from 0.13, 0.18, and 0.09 for scenario 1 to 0.04, 0.06, and 0.04 after applying the filtered training data in the scenario 2. Finally, the overall accuracy of the initial training data (scenario 1) and the filtered training data (scenario 2) were 0.86 and 0.94, respectively. These results underscore the effectiveness of the proposed strategy in enhancing the accuracy of land cover classification within the wetland over time, highlighting its potential for future wetland conservation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".