Multi-Resolution Feature Extraction and Fusion for Traditional Village Landscape Analysis in Remote Sensing Imagery
Why this work is in the frame
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Bibliographic record
Abstract
The complexity and diversity of traditional village landscapes present significant challenges to remote sensing image analysis.Existing methods, such as pixel-level classification, object-based image analysis (OBIA), and deep learning techniques, are often computationally intensive and require powerful hardware support and optimization algorithms.To address these issues, a landscape feature analysis model based on multiresolution feature extraction and fusion with attention pyramid decoding is proposed in this study.By employing multi-scale feature extraction and fusion, this model captures landscape features at various levels and scales, enabling more comprehensive and in-depth analysis of complex remote sensing images.Additionally, the attention pyramid decoding approach adaptively mines spatial and semantic information, enhancing the model's focus on pertinent features and consequently improving classification accuracy.Experimental results confirm the effectiveness of the proposed model for traditional village landscape analysis in remote sensing imagery.
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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 | 0.001 |
| 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