Rock Surface Classification in a Mine Drift Using Multiscale Geometric Features
Bibliographic record
Abstract
Scale-dependent statistical depictions of surface morphology offer the potential to parameterize complex geometrical scaling relationships with greater detail than traditional fractal measures. Using multiscale operators, it is possible to identify points belonging to rough discontinuous surfaces in noisy point clouds solely on the basis of their local geometry. Many strategies for point cloud feature classification have been developed since the proliferation of laser scanning systems. Most of the techniques which are applicable to natural scenes employ external data sources such as hyperspectral imagery, return pulse intensity, and waveform data. In this letter, multiscale geometric parameters are used to identify individual point observations corresponding to rock surfaces in point clouds acquired by terrestrial laser scanning in scenes with man-made clutter and scanning artifacts. Three multiscale operators, namely, the approximate shape and density of a defined neighborhood and the distance of its mean point from its geometric center, are fused into a single feature vector. The procedure is demonstrated using real point cloud data acquired in a mine drift, with the goal of identifying points belonging to the rock face obscured by an overlying wire support mesh. Using the extra-trees classifier, extraneous returns caused by the mesh were excluded from the point cloud with a 97% success rate, while 87% of the desired surface points were retained.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".