Semiautomatic Road Extraction From VHR Images Based on Multiscale and Spectral Angle in Case of Earthquake
Why this work is in the frame
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Bibliographic record
Abstract
Road extraction offers great potential for research initiatives because of the complexity due to its great topological variability. The use of remote sensing imagery to accomplish this mapping is an interesting option. Indeed, satellite images can be acquired shortly after the event, and cover a large area of territory. We hope to produce a mapping of the present facilities from very high resolution images shortly after a disaster. This availability of very high spatial resolution images brings added value to the study in urban areas and their mapping. Increasing the spatial resolution generates noise, which makes extraction difficult, especially in the event of an earthquake in an urban context. This problem increases false alarm rates and generally affects the performance of road extraction algorithms in detecting linear features used to locate and extract roads on such images. During major disasters, short deadlines demand an effective response in terms of updating the mapping of affected areas. Our aim is to improve the road extraction quality after adaptation of Lowe's scale-invariant features transform descriptors jointly with spectral angle algorithms. An illustration is performed on three high-resolution images, respectively, representing a rural, suburban, and urban disaster area, captured by the Quickbird satellite. Our approach significantly reduces the false detection rate and shows an increase in overall quality of up to nearly 30% in some cases as compared to what obtain in the literature.
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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