Bibliographic record
Abstract
Aerial data collection for use in GIS has, in the past, been an expensive undertaking and required significant capital expenditures for metric aerial cameras, aircraft purchase, maintenance, fuel, insurance, and not to mention a significant cost for specialized labour. The advent of Unmanned Aerial Vehicles (UAV’s), and their increasing mainstream use, has created an alternative to conventional aerial photography for the creation of Digital Surface Models (DSMs) and orthophotos. The standards of traditional aerial data collection have been spelt out in long-established guidelines by the American Society of Photogrammetry and Remote Sensing. UAV operators, often non-specialist newcomers to the field, have side-stepped these standards and have no means to ensure the quality and reproducibility of their data. I propose a set of simple best-practices that can be adopted both by UAV operators and their clients to establish some measure of Quality Assurance and Quality Control while maintaining the cost-advantage afforded by UAVs. These practices can be grouped into five main areas: 1) proper camera and lens selection, 2) pre-calibration of cameras used for photogrammetric mapping, 3) establishing accurate ground-control across the area of mapping and not relying on consumer-grade GNSS for air stations, 4) pre-flight calculation of flight parameters based on a clear accuracy requirement, 5) flying at a sufficient height to minimize relief displacement so as to create artifact-free orthophotos. It is hoped that a better understanding of the underlying principles of photogrammetry by both UAV operators and their clients will guarantee the proper implementation of UAVs for high-accuracy GIS data collection in the future.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".