GeoMAE-CLIPNet: Self-Supervised Transformer with Vision—Language Adaptors for Remote Sensing Image Classification
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".