Road damage detection from VHR remote sensing images based on multiscale texture analysis and dempster shafer theory
Bibliographic record
Abstract
Infrastructures damage detection in case of major disasters is one of the most discussed problems and represent an active field of research in remotely sensed imaging. In this paper, a novel method designed for fast roads damage extraction is proposed since these structures are important in the delivery of assistance and to manage the intervention of the emergency teams on ground. The proposed methodology includes first an already completed step that consists in extracting the road network from both the pre- and post-disaster images. Then, a multiscale segmentation based on the wavelet transform is performed on the road surface and the obtained objects from the two coregistered images are compared. Finally, the Dempster Shafer theory is applied to decide the membership class of each object in a first step, and then identify the nature of changes using the multidimensional evidential reasoning. Images acquired by the Geo-Eye satellite before and after the earthquake that hits Port-au-Prince (Haiti) on January 2010 are used in the experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".