Automatic road extraction in rural areas, based on the Radon transform using digital images
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Bibliographic record
Abstract
AbstractManual digitizing of road maps is an expensive and time-consuming task. We present a method based on the Radon transform, which automatically extracts roads in rural areas from digital images. The proposed method iteratively detects linear segments and then generates road centerlines. The proposed method is composed of two phases: in the first, the seed segments are detected, and in the second, road widths are measured and successive detection of line segments that are candidates as road centerlines is performed. Several tests were carried out using aerial photographs (digital images) of a rural area in Brazil, and the results are presented and discussed. The quality of the extracted road centerlines was computed using the following indexes: completeness, correctness, and RMS. The values obtained show that the proposed methodology performs well.La numérisation manuelle des cartes routières est un travail coûteux et long. Dans ce travail, on présente une méthode basée sur la transformée de Radon permettant d'extraire automatiquement les routes dans les zones rurales à partir d'images numériques. La méthode proposée détecte de façon itérative des segments linéaires et génère ensuite les lignes centrales des routes. La méthode proposée comporte deux phases: dans la première phase, les noyaux des segments sont détectés et, dans la deuxième phase, les largeurs des routes sont mesurées puis, une détection des segments de ligne potentiellement associés aux lignes centrales des routes est réalisée. Différents tests ont été réalisés à l'aide de photos aériennes (images numériques) d'une zone rurale située au Brésil et les résultats sont présentés et discutés. La qualité des lignes centrales des routes extraites a été calculée à l'aide des indices suivants: taux de réalisation, exactitude et erreur quadratique (RMS). Les valeurs obtenues montrent la bonne performance de la méthodologie proposée.[Traduit par la Rédaction] AcknowledgementsThe authors thank FAPEMIG (Research Support Foundation of Minas Gerais) for financial support granted for publication of this paper.
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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 | 0.001 |
| 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