SatViT: Pretraining Transformers for Earth Observation
Why this work is in the frame
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Bibliographic record
Abstract
Despite the enormous success of the ’pre-training and fine-tuning’ paradigm, widespread across machine learning, it has yet to pervade remote sensing (RS). To help rectify this, we pre-train a vision transformer (ViT) on 1.3 million satellite-derived RS images. We pre-train SatViT using a state-of-the-art self-supervised learning algorithm called masked autoencoding (MAE), which learns general representations by reconstructing held-out image patches. Crucially, this approach does not require annotated data, allowing us to pre-train on unlabeled images acquired from Sentinel-1 & 2. After fine-tuning, SatViT outperforms state-of-the-art ImageNet and RS-specific pre-trained models on both of our downstream tasks. We further improve overall accuracy (by 3.2% and 0.21%) by continuing to pre-train SatViT—still using MAE—on the unlabelled target datasets. Most importantly, we release our code, pre-trained model weights, and tutorials aimed at helping researchers fine-tune our models. (https://github.com/antofuller/SatViT).
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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 | 0.001 | 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