3D Mapping with High Resolution Images
Bibliographic record
Abstract
High-resolution images, EROS-A1, IKONOS and QuickBird-2 from 2 m to 0.6 m pixel spacing re-spectively, are geometrically processed with a 3D parametric model developed at the Canada Centre for Remote Sensing. A positioning accuracy of one pixel for the ortho-images can be obtained if 7-10 ground control points (GCPs) used in the 3D parametric model computation are better than 1-pixel accurate (carto-graphic and image coordinates) and if the digital terrain model (DTM) used in the ortho-rectification proc-ess is more accurate than 5-m. When the GCPs are less accurate (around 3-5 m) 20 are necessary to avoid the error propagation through the 3D parametric model. Furthermore, a DTM is extracted from stereo IKONOS images using automatic image matching. A general accuracy of 6.5 m (68% level of confidence) when compared to an airborne lidar DTM (0.5 m accurate) is obtained but is correlated with the land covers. However, the accuracy on bare soils improves to 1.5 m (68% level of confidence). Since the sur-face heights are included in DTM, the surface and the height of buildings can be extracted from the stereo IKONOS DTM. Different methods are proposed, which should used an expert system to integrate a priori information on the buildings in the different processing steps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".