Automated extraction of road features using LiDAR data: A review of LiDAR applications in transportation
Bibliographic record
Abstract
Mobile Light Detection and Ranging (LiDAR) integrates laser scanning equipment, Global Positioning Systems, and inertial navigation technologies into one system that can acquire positional data and intensity information about surrounding objects. In Mobile Laser Scanning, data collection equipment is mounted on a truck which travels through a highway creating a 3D point cloud image of the entire road segment. The high point density of such datasets enables automated extraction of multiple features on highways, which are typically collected manually during long site visits. In addition, the LiDAR data sets could also be used to perform geometric assessments of highway attributes such as available stopping sight distance. If used to their full potential, LiDAR datasets could create a paradigm shift in how geometric assessment and safety audits on highways are conducted. Despite the huge potential, only limited research has attempted extraction of geometric design data from the LiDAR images. This could be a matter of researchers not realizing the full potential of such data or believing that, due to their size, processing such datasets might be impractical. To highlight the full potential of LiDAR data in transportation and to address doubts about the feasibility of extracting information from LiDAR images, this paper provides a thorough review of the potential applications of LiDAR in the field of transportation. The paper includes a thorough review of the previous attempts of transportation data extraction from LiDAR while also providing an overview of other applications which researchers are yet to explore. The paper also discusses the challenges associated with the extraction process and future research in this area.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".