Combined Road Segmentation and Contour Extraction for Remote Sensing Images Based on Cascaded U-Net
Bibliographic record
Abstract
Aiming at the problem that the deep-learning-based model for road information extraction can only output single-task results and the inadequate use of correlation between multiple tasks,a combined road segmentation and contour extraction method based on cascaded U-Net is proposed,which extracts the road contour after fusing the feature map of road semantic segmentation with the original image.Firstly,the U-Net network structure is used to extract the hierarchical features of optical remote sensing images,and the cascaded U-Net structure is introduced to concatenate the features to extract the pixel-level label and contours of roads respectively.Secondly,the attention mechanism module is added to each stage of U-Net to extract spatial context information and deep level features to improve the detection sensitivity of details.Finally,the joint loss function composed of dice coefficient and cross-entropy error is used for the overall training to extract simultaneously the road semantic segmentation and contour results.On the optical remote sensing dataset of the urban area of Ottawa,Canada,the joint extraction method of road information based on cascaded U-Net achieves 42% precision,58% recall,48.2% F1 score and 71.6% mIoU in the segmentation index,and achieves a global optimal threshold(ODS) of 0.896 in the road detection index.The results show that,the model can meet the requirements of joint extraction of road multi-task information and has better detection accuracy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".