Corse-to-Fine Road Extraction Based on Local Dirichlet Mixture Models and Multiscale-High-Order Deep Learning
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Bibliographic record
Abstract
Road extraction from remote sensing images is an attractive but difficult task. Gray-value distribution and structure feature information are both crucial for road extraction task. However, existing methods mainly focus on structure feature information which contains morphological shape features and machine learning features, suffering from lots of false positives which are generated at positions having similar structure features but different gray-value distribution with roads. To effectively fuse the two complementary gray-value distribution and structure feature information, we propose a coarse-to-fine road extraction algorithm from remote sensing images. First, at the coarse level, we introduce a local Dirichlet mixture models (LDMM) which utilizing gray-value distribution information to pre-segment images into potential roads and backgrounds. Thus, most backgrounds having different gray-value distribution with roads can be removed firstly. Compared with original Dirichlet mixture models, the LDMM is much faster and more accurate. Next, at the fine level, we introduce a multiscale-high-order deep learning strategy based on ResNet model which can learn robust structure context features for final road extraction step. Based on the results of LDMM, the multiscale-high-order strategy can further remove false positives which have different structure features with roads. Compared with a single scanning size ResNet, our multiscale-high-order strategy can learn higher-order context information, leading to better performances. We test our algorithm on Shaoshan dataset. Experiments illustrate our better performance compared with other six state-of-the-art methods.
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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