Bibliographic record
Abstract
Over the past few years, laser scanning has been established as a leading technology for the acquisition of high density 3D spatial information. Digital Terrain Models (DTMs), which can be used for different engineering applications, are obtained by classification of laser data and removing the points that do not belong to terrain surface. The commonly used methods for the classification of laser scanning data are point-based. The major drawback of these methods is focusing on the discontinuities between neighbouring points regardless of the nature of the objects they belong to, which might lead to unreliable classification results. A segmentation-based approach for the classification of both airborne and terrestrial point clouds is presented in this paper. This approach is designed to overcome the drawbacks of point-based classification methods. As the first step, the laser point cloud is segmented by clustering the points with common attributes. To compute precise attributes, an adaptive neighbourhood of each point is firstly defined while considering the proximity of the points in 3D space, surface trend, and noise level in datasets. Then, the coordinates of the origin’s projection on the best fitted plane to each point’s neighbourhood are computed and used as segmentation attributes. Finally, the laser points with similar attributes are aggregated in the attribute space using a new clustering approach. After segmentation, a heuristic approach is used to classify the segmentation results. The boundaries of segmented surfaces are utilized to determine the adjacency relationship among derived segments. Then, different measures such as the slope and area of each segment, the height difference, and planimetric distance between adjacent segments are checked to classify them into terrain and off-terrain surfaces. The classification of non-segmented points is carried out by comparing the height difference between them and their nearest classified terrain-segments. Experimental results from real data have demonstrated the feasibility of the proposed approach for the classification of airborne and terrestrial laser data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| 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 | 0.001 |
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".