Optimization of Multiresolution Segmentation for Object-Oriented Road Detection from High-Resolution Images
Why this work is in the frame
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Bibliographic record
Abstract
. This study aims to detect roads in high-resolution satellite images via an automatic object-oriented analysis. In this regard, an automatic image segmentation method is proposed to generate appropriate image objects. To achieve this goal, a genetic algorithm optimization with a new cost function is designed to set proper segmentation parameters, including the scale factor and the weights of the shape and compactness heterogeneities. The obtained image segments are then classified as roads or background features, using a fuzzy nearest neighborhood method.The proposed method is implemented on 2 pan-sharpened IKONOS images covering urban areas in the cities of Shiraz and Yazd, Iran. A comparison of these segments with those obtained using traditional manual methods proves the efficiency of this method. Moreover, the entire road detection system is compared with a support vector machine pixel-based classification; the proposed method improved the accuracy by 5%.
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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