DENSIFICATION OF DTM GRID DATA USING SINGLE SATELLITE IMAGERY
Bibliographic record
Abstract
Digital Terrain Models (DTMs) are simply regular grids of elevation measurements over the land surface. DTMs are mainly extracted by applying the technique of stereo measurements on images available from photogrammetry and remote sensing. Enormous amounts of local and global DTM data with different specifications are now available. However, there are many geoscience and engineering applications which need denser DTM grid data than available. Fortunately, advanced space technology has provided much single (if not stereo) high-resolution satellite imageries almost worldwide. Nevertheless, in cases where only monocular images are available, reconstruction of the object surfaces becomes more difficult. Shape from Shading (SFS) is one of the methods to derive the geometric information about the objects from the analysis of the monocular images. This paper discusses the use of SFS methods with single high resolution satellite imagery to densify regular grids of heights. Three different methodologies are briefly explained and then implemented with both simulated and real data. Moreover, classification techniques are used to fine tune the albedo coefficient in the irradiance model. Numerical results are briefly discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".