DIRECT GEOREFERENCING ON SMALL UNMANNED AERIAL PLATFORMS FOR IMPROVED RELIABILITY AND ACCURACY OF MAPPING WITHOUT THE NEED FOR GROUND CONTROL POINTS
Bibliographic record
Abstract
Abstract. This paper presents results from a Direct Mapping Solution (DMS) comprised of an Applanix APX-15 UAV GNSS-Inertial system integrated with a Sony a7R camera to produce highly accurate ortho-rectified imagery without Ground Control Points on a Microdrones md4-1000 platform. A 55 millimeter Nikkor f/1.8 lens was mounted on the Sony a7R and the camera was then focused and calibrated terrestrially using the Applanix camera calibration facility, and then integrated with the APX-15 UAV GNSS-Inertial system using a custom mount specifically designed for UAV applications. In July 2015, Applanix and Avyon carried out a test flight of this system. The goal of the test flight was to assess the performance of DMS APX-15 UAV direct georeferencing system on the md4-1000. The area mapped during the test was a 250 x 300 meter block in a rural setting in Ontario, Canada. Several ground control points are distributed within the test area. The test included 8 North-South lines and 1 cross strip flown at 80 meters AGL, resulting in a ~1 centimeter Ground Sample Distance (GSD). Map products were generated from the test flight using Direct Georeferencing, and then compared for accuracy against the known positions of ground control points in the test area. The GNSS-Inertial data collected by the APX-15 UAV was post-processed in Single Base mode, using a base station located in the project area via POSPac UAV. The base-station’s position was precisely determined by processing a 12-hour session using the CSRS-PPP Post Processing service. The ground control points were surveyed in using differential GNSS post-processing techniques with respect to the base-station.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".