DSM extraction from IKONOS and EROS A stereo imagery: methodology, accuracy and problems
Bibliographic record
Abstract
ABSTRACT: The goal of this work was to evaluate the mean accuracy and its dependency on morphology and land cover types of the digital surface models (DSMs) extracted from IKONOS-II and EROS-A high-resolution satellite in-track stereo imagery. DSMs were generated by the software PCI Geomatica OrthoEngine v. 9.0, which implements the well-known 3D rigorous (physical) model developed at the Canada Centre for Remote Sensing, Natural Resources Canada (CCRS). The paper illustrates the encountered problems and the achieved results during three experiments regarding two stereo IKONOS-II panchromatic images (with a small overlapping) of the Pozzuoli area (Naples, Southern Italy) and one stereo EROS-A image of the Tivoli area (Rome, Central Italy). Ground control points (GCPs) were collected by GPS: in the Pozzuoli area 29 GCPs were rapid-static surveyed with a mean 3D accuracy of 0.2-0.3 m; in the Tivoli area 23 GCPs with a mean 3D accuracy of 0.1 m were collected by RTK survey assisted by the GPS permanent network of the Lazio Region, managed by the Area di Geodesia e Geomatica-Università di Roma “La Sapienza”. Three types of comparison were carried out when possible, in order to assess both mean accuracy and its dependency on morphology and land cover types: sample comparison, based on significant numbers of Independent Check Points (ICP) with a mean 3D accuracy of 0.3-0.5 m collected by kinematic GPS
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".