Practical Studies of Accuracy Enhancement Techniques for Terrestrial Mobile LiDAR Point Clouds in Engineering Surveys
Bibliographic record
Abstract
Improving the accuracy of Terrestrial Mobile LiDAR (TML) data has been a challenge in Engineering Surveys. This research aims at how to innovatively enhance the accuracy of TML solutions through post-processing toward meeting high accuracy specifications in Engineering Surveys. Three techniques are described and implemented. Firstly, the linear feature-enhanced 3D Conformal Coordinate Transformation (3DCCT) is developed by employing ground control points (GCPs) together with linear feature constraints. Secondly, a two-stage Multistrip Adjustment (MA) technique is proposed that first co-register the overlapped TML strips using tie points and tie features extracted from them and then adjust the co-registered LiDAR data by applying the feature enhanced 3DCCT. Lastly, a post-processing technique for calibrating the LiDAR boresight errors of a terrestrial LiDAR system is tested out by using its own point clouds. Their usage has been strategically studied through their applications to field-test data. Specifically, multiple scenarios have been tested, analysed, and compared in terms of the usage of GCPs, the effect of feature constraints, MA and the effect of boresight error compensation etc. As shown from the results, their utilization is encouragingly contributing to the accuracy improvement of TML data towards the high accuracy demand for Engineering Surveys. A practical implementation dataflow is outlined at the end of this manuscript.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".