Automated Method for Detection of Missing Road Point Regions in Mobile Laser Scanning Data
Bibliographic record
Abstract
The paper proposes a method supported by MATLAB for detection and measurement of missing point regions (MPR) which may cause severe road information loss in mobile laser scanning (MLS) point clouds. First, the scan-angle thresholds are used to segment the road area for MPR detection. Second, the segmented part is mapped onto a binary image with a pixel size of ε through rasterization. Then, MPR featuring connected 1-pixels are identified and measured via image processing techniques. Finally, the parameters regarding MPR in the image space are reparametrized in relation to the vehicle path recorded in MLS data for a better understanding of MPR properties on the geodetic plane. Tests on two MLS datasets show that the output of the proposed approach can effectively detect and assess MPR in the dataset. The ε parameter exerts a substantial influence on the performance of the method, and it is recommended that its value should be optimized for accurate MPR detections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".