Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification
Bibliographic record
Abstract
This study was focused on addressing the need for accurate land-use/land-cover classification (LULC) maps in Iran and in other similarly developing countries. To generate and validate a new LULC map for northeastern Iran’s 2037.5 km2 Hable-roud watershed, a step-by-step process was developed and implemented, consisting of image preprocessing, extraction of training and reference sampling locations, decomposition of multi-spectral thematic mapper bands into features by independent component analysis methods, classification using these features and slope maps, enhancement of land-use classes through image segmentation and zonal statistics, then through consideration of normalized difference vegetation index and climatic zones, followed by ground truthing. This newly developed approach provided maps that distinguished dryland farming, irrigated farmland, forest plantations, and low-, medium-, and high-vegetation density rangelands, while currently available maps for the watershed lef t 39% of lands unclassified or in combined classes. The new maps’ ground-truthing-based overall accuracy and kappa coefficient were 88.3% and 0.83, respectively. In order to develop such an improved LULC map, it was necessary to go beyond the mere analysis of reflectance information, to incorporating climatic and topographic data through this newly proposed step-by-step approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".