Gigapixel Imaging and Photogrammetry: Development of a New Long Range Remote Imaging Technique
Bibliographic record
Abstract
The use of terrestrial remote imaging techniques, specifically LiDAR (Light Detection And Ranging) and digital stereo-photogrammetry, are widely proven and accepted for the mapping of geological structure and monitoring of mass movements. The use of such technologies can be limited, however: LiDAR generally by the cost of acquisition, and stereo-photogrammetry by the tradeoff between possible resolution within the scene versus the spatial extent of the coverage. The objective of this research is to test a hybrid gigapixel photogrammetry method, and investigate optimal equipment configurations for use in mountainous terrain. The scope of the work included field testing at variable ranges, angles, resolutions, and in variable geological and climatologically settings. Original field work was carried out in Canada to test various lenses and cameras, and detailed field mapping excursions were conducted in Norway. The key findings of the research are example data generated by gigapixel photogrammetry, a detailed discussion on optimal photography equipment for gigapixel imaging, and implementations of the imaging possibilities for rockfall mapping. This paper represents a discussion about a new terrestrial 3-dimensional imaging technique. The findings of this research will directly benefit natural hazard mapping programs in which rockfall potential must be recorded and the use of standard 3-dimensional imaging techniques cannot be applied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".