Approximate Extraction of Spiralled Horizontal Curves from Satellite Imagery
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
Generating road databases from high-resolution satellite imagery is advantageous over traditional methods because of its simplicity and efficiency. Previous research has addressed the extraction of nonspiralled horizontal curves (simple, compound, and reverse curves). All curves were assumed to be circular. This paper presents an approximate method for extracting spiralled horizontal curves. A spiralled horizontal curve consists of a circular curve and a spiral curve at each end that connects the circular curve and the tangent. The spiral curve has a curvature that gradually increases from zero (at the tangent) to the curvature of the circular curve. Because of the symmetry of the spiralled horizontal curve and the semiautomatic nature of the extraction process, the search is three dimensional. Similar to the extraction of nonspiralled horizontal curves, the proposed method performs the search procedures in a smaller area than the image size and achieves faster computations. The method first extracts one side of the road, and a simple procedure for establishing the other side is then applied. The derived curve parameters (circular curve radius, deflection angle, and spiral length) represent useful inputs into a geographic information system database.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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