Research on Cloud Detection in Non-agricultural Image Based on Long Time Series Data
Bibliographic record
Abstract
With the rapid development of China's economy, the increasingly severe phenomenon of farmland non-agriculturalization may potentially impact China’s food security. Against this backdrop, the task of monitoring farmland non-agriculturalization in Fujian Province has become increasingly arduous, leading to an exponential increase in the amount of remote sensing image data that needs to be received and analyzed throughout the year. Solely relying on manual methods for analyzing the quality of vast amounts of data becomes highly challenging. Therefore, it is imperative to introduce automated detection technology to improve the speed of cloud layer detection in images. This paper proposes an algorithm that utilizes long-term historical sequences of remote sensing imagery to obtain statistical data on the dark channel prior of ground objects, which are then compared with the dark channel prior from the images to be inspected, thereby obtaining information about cloud layers. Experimental validation confirms that the method presented in this paper can achieve automatic cloud layer detection, which to a certain extent improves production efficiency while also delivering relatively satisfactory detection results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".