Generation of a ground-level DEM in a dense equatorial forest zone by merging airborne laser data and a top-of-canopy DEM
Bibliographic record
Abstract
(i) selecting ground points from the airborne data and deducing a pseudo canopy height for these points, (ii) characterizing the canopy height from a statistical and geostatistical standpoint, (iii) kriging the canopy height and subtracting the resulting model from the top-of-canopy DEM to obtain a DEM corresponding to a ground-level DEM, and (iv) validation. Validation consists of comparing the results with topographic maps and a local heliborne DEM and studying the relationship between the configuration of the airborne trials, in terms of flight-line spacing, and accuracy of the resulting kriged DEM. The results show that the accuracy of the kriged ground-level DEM is significantly better than that of the initial radargrammetric DEM. The standard deviation of elevation errors is reduced from 21.2 to 11.9 m or from 25.3 to 11.1 m, depending on the validation source adopted (French National Geographic Institute spot heights and local heliborne DEM, respectively). In addition, the relationship between flight-line spacing and accuracy of the resulting kriged DEM helps estimate what flight-line spacing is needed to obtain a given accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".