Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission
Bibliographic record
Abstract
The first near‐global high‐resolution digital elevation model (DEM) of the Earth has recently been released following the successful Shuttle Radar Topography Mission (SRTM) of 2000. This data set will have applications in a wide range of fields and will be especially valuable in the Earth sciences. Prior to widespread dissemination and use, it is important to acquire knowledge regarding the accuracy characteristics. In this work a comprehensive analysis of the vertical errors present in the data set and the assessment of their effects on different hydrogeomorphic products is performed. In particular, the work consisted of (1) measuring the vertical accuracy of the data set in two areas with different topographic characteristics; (2) characterizing the error structure by comparing elevation residuals with terrain attributes; (3) assessing a wavelet‐based filter for removing speckle; and (4) assessing the effects of vertical errors on hydrogeomorphic products and on slope stability modeling. The results indicate that in the two sites, relief has a strong effect on the vertical accuracy of the SRTM DEM. In the high‐relief terrain, large errors and data voids are frequent, and their location is strongly influenced by topography, while in the low‐ to medium‐relief site, errors are smaller, although the hilly terrain still produces an effect on the sign of the errors. Speckling generates deviations in the drainage network in one of the investigated areas, but the application of a wavelet filter proved to be an effective tool for removing vertical noise, although further fine tuning is necessary. Vertical errors cause differences in automatically extracted hydrogeomorphic products that range between 4 and 1090.
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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 | 0.002 | 0.006 |
| 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) | 0.000 | 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 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".