A comparison between LiDAR and photogrammetry digital terrain models in a forest area on Tenerife Island
Bibliographic record
Abstract
This paper compares two types of digital terrain models (DTMs) with ground elevation measures collected through field work in a dense forest area on the island of Tenerife (Canary Islands, Spain). The first was an existing DTM derived from altimetric features obtained by manual photogrammetric restitution. The second DTM was computed from aerial LiDAR data with a nadir density of 0.8 points·m−2. Both DTMs have a pixel size of 5 m. The field work consisted of measuring three elevation profiles by land surveying techniques using a total station survey and taking into account different vegetation covers. The analysis of the profiles by means of nonparametric techniques showed an accuracy at the 95th percentile between 0.54 m and 24.26 m for the photogrammetry-derived DTM and between 0.22 m and 3.20 m for the LiDAR-derived DTM. Plotting the elevation profiles allowed for the visual detection of locations where the models failed. The LiDAR data were able to reflect more accurately the true ground surface in ar...
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".