Multi-dimensional Attention UNet with Variable Size Convolution Group for Road Segmentation in Remote Sensing Imagery
Why this work is in the frame
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Bibliographic record
Abstract
The information extraction of high-resolution remote sensing images is an increasingly important part in essential urban planning, geological survey, and disaster monitoring. High-resolution feature information helps us quickly understand the arrangement of ground targets in an area. This article proposes a model called MDAUNet with multi-dimensional attention module, based on different dimensional information of road feature maps for attention. Besides, we use a variable size convolution group (VCG) module in resnet embedded to obtain better road representation. At the same time, to obtain road feature information at different levels in the decoding part, we adopt the dense connection of the decoding part to optimize the feature map of the decoding part. Our proposed road multi-dimensional information attention network has achieved superior performance on the Ottawa road dataset and CHN-CUG road dataset, its performance far exceeds the national art level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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