Detection and Location of Buried Infrastructures Using Ground Penetrating Radar
Bibliographic record
Abstract
This article proposes an approach to improve the deployment of ground penetrating radar (GPR) in the field to detected and locate urban infrastructures. It consists of exploiting geographic data layers, database management systems, and a WebGIS, allowing users to handle GPR data within a georeferenced environment is developed based on a platform called GVX, providing users with four features, being (1) map integration, (2) geo-annotations and points of interest interaction, (3) radargram georeferencing, and (4) georeferenced slice visualization. Experiments with two categories of users, expert and non-expert GPR practitioners, have been performed. Based on the users' evaluation, the approach is valuable and can significantly improve GPR deployment. It helps users when discovering unmapped underground objects, delimiting the survey area, and interpreting GPR complex datasets. Overall, the approach optimized time and facilitated the spatial notion between GPR profiles and 3D meshes with map resources, allowing users to produce reliable maps, conforming to geospatial standards (CityGML).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".