Automatic Cloud Detection and Removal in Satellite Imagery Using Deep Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid advancement of remote sensing technology, satellite imagery has become increasingly vital in global geographic information systems, environmental monitoring, and resource management.However, cloud cover frequently degrades the quality of satellite images, limiting their effectiveness in many critical areas.Traditional methods for cloud detection and removal, such as threshold analysis and spectral feature analysis, often fail to achieve satisfactory results due to environmental constraints and algorithmic limitations.In response, this study employs deep learning techniques, specifically superpixel segmentation and generative adversarial networks (GAN), to address this issue.This paper begins by discussing the importance of cloud detection and removal in satellite imagery and reviews existing major techniques and methods.It then explores the application of superpixel segmentation based on local adaptive distance for automatic cloud boundary identification, along with innovative applications of GAN for surface information reconstruction in cloudcovered areas.These methods not only enhance the accuracy of cloud detection but also effectively optimize the cloud removal process, paving the way for further applications of satellite imagery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it