Bibliographic record
Abstract
The segmentation of Laser scanning data into planar features is a crucial first step in the post processing of Laser data. Classically, a planar segmentation procedure is performed over a single scan, or a group of homogeneous and co-registered scans. In this work, we address cases where the datasets used vary significantly in terms of local point density, relative point accuracy, and the collection method (i.e., ground versus airborne). First, an initial segmentation procedure is performed over individual strips to collect vital pieces of information about the scan nature such as local point densities, approximate surface roughness and surface normal of segmented regions. Once the aforementioned information is collected, and the registration of overlapping scans is verified, the combined segmentation stage may begin. In the combined case, a recursive weighted least squares and a region-growing-based algorithm is adopted. Weighting of individual points is estimated using the information collected in the initial segmentation step. Using the proposed combined segmentation is beneficial for multiple reasons: (1) missing or incomplete features in one or more scans are likely to be complete in the combined dataset, (2) the overall increased point density is very beneficial for detecting finer planar patches, and the weighted least squares will ensure that the integrity of the combined dataset is maintained, and (3) the results of the combined segmentation is useful to verify the correctness of the registration procedure. In this paper, we present a case study of two sets of airborne scans and one set of six tripod mounted laser scans. Results demonstrate improved extraction of planar features from the combined dataset as appose to segmenting individual strips.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".