Assessing Digital Elevation Model Uncertainty Using GPS Survey Data
Bibliographic record
Abstract
The use of terrain and elevation data is critical for a number of applications in science and engineering. Typically, the quality of digital elevation models (DEMs) is assessed using external and independent point data sources to arrive at an overall RMS value for the errors. The utility of such a single-valued overall assessment depends on the spatial extent of the area under consideration and the terrain variability (both over time and space), as well as the application requirements. This paper aimed to understand the suite of parameters that are important to consider in deriving a DEM error budget. Specifically, terrain slope, land-cover type, information loss, and data measurement schemes were investigated. A region in western Canada spanning the Rocky Mountains was used to numerically quantify errors using two Global Positioning System (GPS) datasets: precise point positioning (PPP) profiles and GPS on benchmarks. Three digital elevation models [Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM2), Shuttle Radar Topography Mission 1 Arc-Second Digital Elevation Model Version 3 (SRTM1v3), and Canadian Digital Elevation Model (CDEM)] were assessed. Results highlight the importance of selecting ground-control points based on the region’s characteristics (e.g., slope, tree cover). This leads to more representative RMS values that improve DEM uncertainty estimations. Finally, a mathematical method [projection onto convex sets (POCS)] for filling data gaps in the GPS data profiles was implemented, and results demonstrate the utility of this approach over conventional interpolation schemes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".