STRIP ADJUSTMENT USING CONJUGATE PLANAR AND LINEAR FEATURES IN OVERLAPPING STRIPS
Bibliographic record
Abstract
LiDAR (Light Detection And Ranging) technology has demonstrated its capabilities as a prominent technique for the acquisition of accurate topographic information with high-density. A LiDAR system consists of three main components: GPS, IMU, and laser units. Data collection is carried out in a strip-wise fashion and the ground coordinates of the laser footprints are derived using the direct geo-referencing information furnished by the onboard GPS/IMU. Due to systematic errors in the LiDAR components and/or alignment, adjacent LiDAR strips usually show discrepancies. Such discrepancies are caused by missing or improperly employed calibration and operational procedures. The ideal solution for the adjustment of neighboring strips is the implementation of an accurate calibration procedure. However, such a calibration demands the original observations (GPS, IMU and the laser measurements), which are not usually available to the end-user. In this work, a strip adjustment procedure for reducing or eliminating discrepancies between overlapping LiDAR strips is proposed. The mathematical model employed is similar to that used in the photogrammetric Block Adjustment of Independent Models (BAIM). Generally, a traditional BAIM uses conjugate points. These features, however, are not suitable for LiDAR surfaces since it is almost impossible to identify conjugate points in overlapping LiDAR strips. In this work, the use of planar patches and linear features, which are represented by sets of non-conjugate points, is investigated. The noncorrespondence of the selected points along the planar and linear features is compensated for by artificially expanding their variance-covariance matrices. The paper presents experimental results from real data illustrating the feasibility of the proposed procedure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".