On the estimation of geological surface roughness from terrestrial laser scanner point clouds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the geosciences, fine-scale detail of geomorphic surfaces, commonly parameterized as roughness, is growing in importance as a source of information for modeling natural phenomena and classifying features of interest. Terrestrial light detection and ranging (LiDAR) scanning (TLS), now well known to geologists, is a natural choice for collecting geospatial data. While many recent studies have investigated methodologies for estimating surface roughness from point clouds, research on the influence of instrumental bias on those point clouds and the resulting roughness estimates is scant. A scale-dependent bias in TLS range measurements could affect the outcome of studies relying on high-resolution surface morphology. Growing numbers of research applications in geomorphology, neotectonics, and other disciplines seek to measure the roughness of surfaces with local topographic variations (referred to as asperities) on the order of a few centimeters or less in size. These asperities may manifest as bed forms or pebbles in a streambed, or wavy textures on fault-slip surfaces. In order to assess the feasibility of applying TLS point cloud data sets to the task of measuring centimeter-scale surface roughness, we evaluated the relationship between roughness values of dimensionally controlled test targets measured with TLS scans and numerical simulations. We measured and simulated instrument rangefinder noise to estimate its influence on surface roughness measurements, which was found to decrease with increasing real surface roughness. The size of the area sampled by a single point measurement (effective radius) was also estimated. The ratio of the effective radius to the radius of surface asperities was found to correlate with the disparity between measured and expected roughness. Rangefinder noise was found to overestimate expected roughness by up to ∼5%, and the smoothing effect of the measurement size disparity was found to underestimate expected roughness by up to 20%. Based on these results, it is evident that TLS point cloud geometry is correlated with instrument parameters, scan range, and the morphology of the real surface. As different geological applications of TLS may call for relative or absolute measurements of roughness at widely different scales, the presence of these biases imposes constraints on choice of instrument and scan network design. A general solution for such measurement biases lies in the development of calibration processes for TLS roughness measurement strategies, for which the results of this study establish a theoretical basis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 0.002 |
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 it