Change detection using multiscale segmentation and Kullback-Leibler divergence: Application on road damage extraction
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of change detection from very high resolution remotely sensed images and its application on road damage extraction in case of major disaster. The proposed methodology is based on the multiscale image segmentation using the Haar wavelet in order to define the appropriate unit of analysis for the comparison step. The Kullback-Leibler divergence is then applied as a similarity measurement to identify changed regions. This strategy is adapted to solve the road damage extraction problem by applying the Dempster-Shafer theory (DST). The images acquired during the earthquake that hits Port-au-Prince (Haiti) on 12 January 2010 are used in the experimentations and the obtained results demonstrate the accuracy and the efficiency of the described method.
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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 | 0.001 |
| 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