Residual U-Net with Attention for Detecting Clouds in Satellite Imagery
Why this work is in the frame
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Bibliographic record
Abstract
Semantic segmentation of clouds in Earth observation imagery is an important task in a variety of remote sensing contexts: from the application of atmospheric corrections to being able to accurately omit cloud pixels when extracting information about ground features. Here we introduce a deep learning approach based on the popular U-Net architecture. The core of the architecture is an U-Net with residual units that ease the training of the network. An attention mechanism is also incorporated to enable the model to more effectively learn and distinguish between cloud and non-cloud features. We also explore two complementary loss functions, Binary Cross Entropy and Jaccard, in order to overcome data imbalances common to this application. Our model is trained on a uniquely curated dataset spanning a wide variety of resolutions, scene contexts, lighting conditions, and seasonality. Our experiments demonstrate that this model is an accurate and robust model for the semantic segmentation of clouds in satellite imagery, and the model achieves state-of-the-art performance over many other models (including others based on CNN architectures) on common benchmark datasets, even without having been exclusively trained on images from the sources in those datasets.
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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 | 0.001 |
| 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