A Vehicle-Based Laser System for High-Resolution DEM Development – Performance in Micro-topography Measurement.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".