Integrating UAV-based LiDAR and imaging data for semi-automated detection of water ponding in pavement networks
Bibliographic record
Abstract
This study leverages unmanned aerial vehicles (UAV) by collecting LiDAR data and RGB imagery to detect pavement surface irregularities prone to water ponding. Such areas can impose safety risks to road users, especially under severe weather conditions. The collected data were georeferenced and imported to an ArcGIS platform to enable surface hydrological simulation. The feasibility of the proposed approach is evaluated through three case studies in an urban setting. Superimposing the surface characteristics obtained from the UAV with the hydrological models could allow for semi-automated identification of the hotspots of hydroplaning as well as accelerated moisture damage in a timely manner, which would be otherwise impractical to detect through manual inspections. Use of UAV for large-scale data collection proved to be cost-effective and timely. This approach offers a scalable solution for urban planners and infrastructure managers aiming to enhance transportation safety.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".