WSCurLe: Weakly Supervised Curriculum Learning for Foundational Vision and Language Architectures in Digital Soil Mapping
Bibliographic record
Abstract
Effective soil sampling is crucial for modern agricultural practices, requiring precise identification of viable agricultural land while excluding non-agricultural areas. While existing remote sensing approaches can distinguish broad land-use categories, they often lack the precision needed for agricultural applications. Our previous work introduced SLVVA, a satellite-based framework for land viability analysis, but its performance was limited by the representation capabilities of its vision-language encoders. In this paper, we propose WSCurLe, a novel weakly supervised curriculum learning approach that progressively fine-tunes vision-language encoders to enhance their semantic understanding while preserving fine-grained image details. Through a series of alternating training stages combining contrastive learning and reconstruction-based methods, WSCurLe significantly improves the quality of land viability segmentation. We demonstrate the effectiveness of our approach through comprehensive comparisons with state-of-the-art vision and language encoders, showing substantial improvements in both efficiency and accuracy for agricultural land analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".