A reliable and fast ribbon road detector using profile analysis and model‐based verification
Bibliographic record
Abstract
Ribbon roads are typical and important geospatial features. In this paper, we proposed a model‐based method for extracting ribbon road features from remotely sensed imagery. Firstly, by analysing the perpendicular profiles along the road direction, we use binary profile template matching along the crossing‐directions to obtain the coarse candidate road centre points. After tracing the curve segments and analysing the perpendicular profiles, the width of the road ribbons and their lateral sides and the accurate centrelines are located. Secondly, a quantitative ribbon road model, which integrates the geometry and radiometry characteristics, is deployed to verify each extracted road segment. The coarse‐to‐fine method makes use of an explicit road profile model and overcomes the negative influences of asymmetrical lateral contrast and width variation. Finally, the model‐based verification enables more reliable sequential processing, such as perceptual grouping. We have conducted extensive experiments on verifying the algorithm. It has been demonstrated that the developed method is highly reliable for automatic detection of the typical ribbon road features from imagery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".