A reliable and fast ribbon road detector using profile analysis and model‐based verification
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
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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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