QUANTITATIVE EVALUATION AND QUALITY CONTROL OF COMMERCIALLY ADOPTED TRADITIONAL AND MODERN LIDAR SYSTEM CALIBRATION TECHNIQUES
Bibliographic record
Abstract
LiDAR systems have been widely adopted for the collection of topographic data. By integrating the information gathered by navigation sensors (GPS/INS) and a laser ranging/scanning unit, LiDAR systems can directly provide the 3D coordinates of a surface at a high density. In the past decade, LiDAR technology has undergone significant improvements in performance (e.g., higher pulse repetition frequencies, higher operational altitudes) and data processing/post-processing methodologies. Major advances in the data processing include more robust methodologies for the system calibration. Implemented traditional calibration by the service providers are based on iterative sequential estimation of the system parameters that require manual adjustment and time-intensive interaction of a trained operator. In the past few years, automated and more accurate methodologies have become commercially available and have been currently in use by some data providers. In this paper, traditional and modern LiDAR system calibration procedures are evaluated and compared. For that purpose, a practical quality control procedure is used. The underlying concept of the quality control procedure is that in the absence of biases in the system parameters (i.e., for a properly calibrated system), conjugate surface elements in overlapping strips should coincide with each other as well as possible. Incompatibilities between conjugate surface elements in overlapping strips can be used to evaluate the quality of the calibration process. In addition, the presented quality control procedure can be used for diagnosing the cause of detected incompatibilities. More specifically, the detected incompatibilities can be used for estimating the remaining biases in the system parameters. Another advantage of the introduced quality control procedure is the possibility of its implementation by the end user since it only requires the LiDAR point cloud coordinates as well as a general knowledge of the flight configuration. Experimental results have demonstrated significant improvements in the quality of fit among overlapping LiDAR strips when using modern LiDAR system calibration procedures and the ability of the proposed quality control approach to detect and eliminate remaining biases in the system parameters.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".