Bibliographic record
Abstract
Most geoscientific applications using georeferenced cartographic/geospatial data require good knowledge and visualization of the topography of the Earth's surface. For example, mapping of geomorphological features is hardly feasible from a single image; three‐dimensional (3D) information has to be generated or added for a better interpretation of the two‐dimensional data. Since the early emergence of earth observation satellites, researchers have investigated different methods of extracting 3D information using satellite data. Since the early experiments with the Earth Terrain Camera flown onboard SkyLab in 1973 to 1974, various analogue or digital sensors in the visible or microwave spectrum have been flown to provide researchers and geoscientists with spatial data for extracting and interpreting 3D information of the Earth's surface. Stereo viewing using digital scanner images, such as with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) along‐track sensors, was, and still is, the most common method used by the mapping, geomatic, and geoscientific communities for generating digital elevation models (DEMs). This paper will review the basic characteristics of stereoscopy and its application to the ASTER system for DEM generation. It will thus address the methods, algorithms and commercial software to extract absolute or relative elevation and assess their performance using the results from various research and commercial organizations. It will finally discuss the use of stereo ASTER DEMs for different geomatic and geoscientific applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".