NEW STRATEGIES FOR INTEGRATING PHOTOGRAMMETRIC AND GNSS DATA
Bibliographic record
Abstract
GNSS-controlled photogrammetry is a mature technology that has found near universal acceptance in the aerial mapping community. The current strategy for integrating photogrammetric and GNSS data is to first process the GNSS data using a stand-alone kinematic Kalman filter processor, and then to use the resulting positions as parameter observations in a photogrammetric bundle adjustment. The utility of this implementation has been well-proven; however, there has been little research into other integration strategies. In this paper, investigations are made into some alternative integration approaches. Focus is given to two techniques: first, an approach with improved information-sharing between the GNSS and photogrammetric processors, and second, a combined least-squares adjustment of the raw GNSS and photogrammetric measurements. After providing background on the existing integration strategies, the new approaches are introduced and detailed. Tests are made using a standard aerial block, results from which appear to indicate that the new techniques do not improve mapping accuracy over the conventional approach. The new techniques, however, may improve GNSS positioning accuracy or enable some more unique network configurations. 1
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".