Utilizing deep learning models and LiDAR data for automated semantic segmentation of infrastructure on multilane rural highways
Bibliographic record
Abstract
This paper presents two Transformer-based approaches for automating the extraction of rural multilane highway infrastructure elements from light detection and ranging data. The first approach uses the Point Transformer v2 model with four additional attributes as input, while the second adapts self-attention and cross-attention mechanisms for point-wise classification. Experiments on 2.5 km of highway in Alberta, Canada, demonstrate the effectiveness of both methods. The first approach achieved a mean Intersection over Union (IoU) score of 78.29% and a mean F1 score of 86.48%, with most class accuracies exceeding 95%. The second method achieved a mean IoU score of 86.03% and a mean F1 score of 92.21%. This research advances automated infrastructure extraction techniques, providing transportation agencies with efficient inventory methods for rural highway infrastructure. The study has implications for autonomous driving, crash environment reproduction, highway safety understanding, big data analysis, maintenance planning, and asset management, highlighting its relevance and importance in modern transportation systems.
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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 | 0.001 |
| 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 | 0.001 |
| 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".