ASTER DEMs for geomatic and geoscientific applications: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it