High-accuracy topographical information extraction based on fusion of ASTER stereo-data and ICESat/GLAS data in Antarctica
Bibliographic record
Abstract
In order to better support Antarctic inland ice sheet expedition from Zhongshan Station to Dome A,the topographic data are necessary.At present,although the entire Antarctic DEM provided by RAMP(Ra-darsat Antarctic Mapping Project) was estimated at the highest horizontal(spatial) resolution of about 200 m,the real horizontal resolution of the DEM varies from place to place depending on the density and scale of the original source data.For ice shelves and the inland ice sheet,the horizontal resolution is about 5 km;the vertical accuracy is estimated to be ±50 m in interior East Antarctic ice sheet and away from the mountain ranges.Therefore,more accurate topographic data are unavailable in Antarc-tica.In order to meet the requirements of high-accuracy topographic information for further researches,this paper mainly addresses a fusion study of ASTER stereo pairs and ICESat/GLAS altimetry data for extraction of high-accuracy DEM in East Antarctica,based on the high horizontal resolution(15 m) of ASTER and vertical accuracy(13.8 cm) of ICESat/GLAS.First,some altimetry data were selected as vertical control points to reduce errors of image correlation matching during the extraction of ASTER-based DEM.Second,ice sheet altimetry data derived from ICESat were used to generate DEM ranging from 75° to 81°S because existing ASTER data do not cover this area and high density of the coverage of ICESat altimetry data.Finally,the DEM in coverage of the expedition route was produced.The analysis of result reveals that the DEM accuracy is improved significantly.The absolute vertical accuracy of DEM is higher than 15 m in some cases and higher than 30 m for all the areas along the expedition route except from the 009-001 scene;the interior accuracy is higher than 15 m and higher than 7 m in some cases.It can meet the requirements of topographic map at 1:50000 scale,which is an economic and advantageous method to produce the topographic products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".