Feasibility of extracting highway vertical profiles from LiDAR data
Bibliographic record
Abstract
Producing as-built drawings is an important task in any road construction project. In fact, in an ideal situation, these drawings must be updated whenever major maintenance work takes place. Unfortunately, constantly updating those drawings is not always feasible due to the amount of manual work associated with the data collection in traditional surveying practice. The increase in computing power and the advancement in technology has led many transportation agencies to consider utilizing remote sensing techniques to extract roadway design features and prepare as-builts of roads. In this note, a procedure to generate as-built drawings of vertical profiles on highways using light detection and ranging (LiDAR) point cloud data are proposed. The procedure is a multistep procedure where the road centerline of each segment is first defined, after that a best fit alignment of points along the road’s centerline is generated. A digital surface model (DSM) of the LiDAR highway is created and the centerline is relayed onto the DSM before generating the road profile. The proposed method is tested using LiDAR data collected on two highways in the province of Alberta, Canada. The profiles extracted using the proposed method are compared against vertical profiles that were generated for the same segments using data collected in GPS surveys and as-built drawings developed in manual surveys. The results show the feasibility of accurately extracting road profiles from LiDAR data. The average difference in grades estimated using the proposed method and the GPS data ranged from 0.023% to 0.061%. In fact, the proposed method was able to capture details in the road profile that were not detected using GPS data, demonstrating the value of using LiDAR for road profile extraction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".