MSHCCT: A Multiscale Compact Convolutional Network for High-Resolution Aerial Scene 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 growing popularity of vision transformers (ViTs) in remote sensing image classification is due to their ability to effectively capture long-range dependencies. However, their high computational cost and memory footprint limit their applicability, particularly for small-scale datasets and resource-constrained environments. To address these challenges, we propose the multiscale multihead compact convolutional transformer (MSHCCT), a lightweight yet powerful model that integrates convolutional tokenization with small-scale ViTs to enhance multiscale feature representation while maintaining computational efficiency. Despite a modest increase in parameters and training time, MSHCCT achieves superior classification accuracy and robustness on high-resolution aerial scenes. Importantly, our approach eliminates the need for model pretraining, additional datasets, or multisensor data fusion, ensuring a computationally efficient and practical solution for remote sensing applications. The code will be made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aj1365/MSHCCT</uri>
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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