Road Curbs Extraction from Mobile Laser Scanning Point Clouds with Multidimensional Rotation‐Invariant Version of the Local Binary Pattern Features
Bibliographic record
Abstract
Abstract Road curb is one of the important components of road information, and its high‐precision information is significant for the development of autonomous driving, intelligent transportation and smart cities. A mobile laser scanning (MLS) system can acquire high‐precision and high‐density road three‐dimensional (3D) point clouds data, which has the advantages of high efficiency, low cost and non‐contact. However, how to extract accurate road information from the massive and disordered point clouds is one of the current research priorities and difficulties. This paper presents a new method to extract the road curbs from the MLS point clouds. The proposed method mainly includes three steps: pre‐processing, road curbs extraction and vectorisation. Pre‐processing obtains the ground, including road subsection and ground identification. Road curbs are first quantitatively represented by the rotation‐invariant version of the local binary pattern (LBPROT) values in three dimensions, including spatial elevation mode, spatial dispersion mode and spatial shape mode, and then they are extracted by a multidimensional LBPROT features semantic recognition model. Vectorised road curb polylines are connected by accurate road curbs points, which are obtained through simplification and denoising. The proposed method was tested on two large‐scale datasets collected from arterial roads and expressways, respectively. The precision of the results was > 95%, recall was > 90% and the F1 score was > 0.93. The experimental results show that the proposed method can effectively extract road curbs in different environments and has robust adaptability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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".