Reflectance modelling using terrestrial LiDAR intensity data
Bibliographic record
Abstract
With the increasing use of Terrestrial Laser Scanners (TLSs) to sense various environments it becomes increasingly necessary to develop automated processing techniques to deal with the large amount of data generated. To aid in the automatic processing, researchers have recently been turning to the use of “intensity” data returned by TLSs as an additional source of information. Ideally a value that is independent of distance and incidence angle, and that instead is related to the surface properties being scanned is desired. For diffuse surfaces this value is termed the reflectance. A method for modelling the reflectance of a diffuse surface using returned intensity, angle of incidence and range obtained from TLSs is presented. The model is applied to two different TLS instruments, a Faro Focus3D and Riegl VZ-400. A model is parametrized for each instrument using data obtained in an underground potash mine. For the Riegl instrument the model is verified using a data set obtained above ground, in a grass playing field. The standard deviation of error is 0.064 or 6.4%. For the Faro instrument the model is obtained using only a subset of the acquired data set and verified with the remainder. The standard deviation for the Faro model is 0.061 or 6.1%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".