GeoMAE-CLIPNet: Self-Supervised Transformer with Vision—Language Adaptors for Remote Sensing Image Classification
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
The present paper introduces GeoMAE-CLIPNet, a complex self-supervised Transformer network that is capable of achieving high-precision remote sensing image classification. The model combines a masked autoencoder (MAE) to learn spatial spectral features and a lightweight vision-language adaptor to learn semantics. In pretraining GeoMAE-CLIPNet has the capability to reconstruct masked patches of image, which can also achieve intrinsic features and interpretability by using reconstructed outputs. Fine-tuning makes use of classification heads that have been trained with cross-entropy and reconstruction regularization losses, which is guaranteed to be healthier when they are few. Experiments are held on benchmark datasets like EuroSAT, AID, and NWPU-RESISC45 and are compared with the recent state-of-the-art models like RemoteCLIP, Scale-MAE, and LDBST. Findings show that the proposed framework produces better classification accuracy, macro-F1 score, and reconstruction fidelity, which proves the effectiveness of the proposed framework in large-scale and heterogeneous satellite imagery. Interpretability is also achieved with the inclusion of reconstruction outputs which helps to understand the class-wise discriminative regions better.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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