Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases
Why this work is in the frame
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Bibliographic record
Abstract
Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs.
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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) | 0.000 | 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