A Vehicle-Based Laser System for High-Resolution DEM Development – Performance in Micro-topography Measurement.
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Bibliographic record
Abstract
A vehicle-based laser measurement system was developed to measure the surface microtopography and to generate high-resolution digital elevation models (DEM). The accuracy of the system in microtopography measurement was evaluated in the laboratory by comparing the DEM data generated by this system with that generated by a more accurate, stationary laser profile meter for several surfaces, including an artificial sand-stone-ridged surface. DEM data was created by interpolating the 3D raw data into a regular, square grid using a two-dimensional, distance-weighted interpolation algorithm. The DEMs were compared using an image-matching method to calculate the correlation coefficient. A test to study the effect of ambient light on elevation measurement under indoor and outdoor environments was also conducted. Correlation coefficients greater than 0.935 were achieved between the DEMs measured by the vehicle-based system and the stationary laser profile meter. The correlation coefficients among the four replications of the DEMs measured by the vehicle-based system were greater than 0.988, indicating that the vehicle-based laser system can provide consistent elevation measurements. Correlation coefficients among the DEMs of the sand-stone-ridged surface measured by the vehicle-based system at different times of the day and under different indoor fluorescent lighting conditions were all above 0.982. Correlation coefficients among DEMs taken at different times of the day and under different outdoor sunlight conditions were all above 0.971. These results indicated that neither the fluorescent light nor the sunlight had a significant effect on the measurements obtained by the vehicle-based laser system. The system provided consistent elevation measurements under both indoor and outdoor lighting conditions.
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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 | 0.001 | 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) | 0.000 | 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 it