DEM Control in Arctic Alaska With ICESat Laser Altimetry
Bibliographic record
Abstract
Use of Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry is demonstrated for control of a digital elevation model (DEM) that is synthesized from repeat-pass ERS-1 and 2 synthetic aperture radar (SAR) imagery using interferomet-ric SAR (InSAR). Our study area is 15 650 km2of the Barrow, AK coastal plain adjacent to the Arctic Ocean; a vast expanse of tundra, lakes, and arctic wetlands of such low relief as to be nearly devoid of terrain features. The accuracy of the ICESat-derived elevation measurements is assessed by comparison with differential global positioning system (DGPS) data acquired along ICESat ground tracks. The ICESat-derived elevations have a mean accuracy, relative to the DGPS elevations, of -0.01 plusmn 0.18 m. ICESat-derived elevations on the Arctic coastal plain provide an excellent source for DEM control. We employ the ICESat-derived ground control points (GCPs) in two distinct InSAR processing steps: 1) selected points are used to perform baseline refinements, which improves the ERS-1 and 2 interfero-grams and 2) the ICESat-derived GCP position data (latitude, longitude, elevation) are then used as control in mosaicking multiple InSAR-derived DEMs. The resulting ICESat-controlled DEM has a mean accuracy of -1.11plusmn 6.3 m relative to an independent standard, which is a commercial airborne InSAR-derived DEM having 0.5 m rms accuracy. This easily meets DTED-2 standards and suggests that DEMs derived using only ICESat altimetry for ground control would meet similar standards in other regions of low relief.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".