Bibliographic record
Abstract
Abstract. High-end consumer quadcopter UAVs or so-called “prosumer devices”, have made inroads into the mapping industry over the past few years, arguably displacing more expensive purpose-built systems. In particular, the DJI Phantom series quadcopters, marketed primarily for videography, have shown considerable promise due to their relatively high-quality cameras. Camera pre-calibration has long been a part of the aerial photogrammetric workflow with calibration certificates being provided by operators for every project flown. Most UAV data, however, is processed today in Structure-from-Motion software where the calibration is generated “on-the-fly” from the same image-set being used for mapping. Often the scenes being mapped and their flight-plans are inappropriate for calibration as they do not have enough variation in altitude to produce a good focal-length solution, and do not have cross-strips to improve the estimation of the principal point. What we propose is a new type of flight-plan that can be run on highly textured scenes of varying height prior to mapping missions that will significantly improve the estimation of the interior orientation parameters and, as a consequence, improve the overall accuracy of projects undertaken with these sorts of UAV systems. We also note that embedded manufacturer camera profiles, which correct for distortion automatically, should be removed prior to all photogrammetric processing, something that is often overlooked as these profiles are not made visible to the end user in most image conversion software, particularly Adobe’s CameraRAW.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".