Enhanced Multi-Level Deep Fusion UNET with Dual Attention for Land Cover Segmentation
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 land use land cover dataset is created using images from ArcGIS, categorized into barren land, crop land, vegetation and water bodies. The dataset is preprocessed for accuracy and diversity, and then used for land cover segmentation and classification using a highly enhanced multi-level deep fused U-Net with dual attention framework (MLDUNet-DA). The proposed architecture includes fused dilated convolution, multi-kernel-based feature recognition and Adaptive Batch Normalization. The training efficiency is improved by an adaptive weight decay factor, and dual attention enhances classification performance to strengthen the generalization of the U-Net segmentation using a Self-adaptive Hippopotamus optimization algorithm. The model is implemented on the Python platform and analyzed for accuracy, Dice score, Jaccard, IoU (Intersection Over Union), precision, recall and F1-Measure.
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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