Air-to-Air Simulated Drone Dataset for AI-powered problems
Bibliographic record
Abstract
This paper introduces the multi-view Air-to-Air Simulated Drone Dataset (A2A-SDD), a comprehensive simulated drone dataset captured using AirSim<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">©</sup>. The dataset encompasses diverse scenarios where one or two drones are pursued by one to three monitoring drones. It includes five types of drones, such as DJI models and a generic quadrotor model, recorded in various weather conditions and environments. Both loaded and unloaded drones are represented, and the dataset provides extensive annotations, including object detection and XYZ co-ordinates. The dataset offers potential applications in training deep learning-based models for counter-UAV measures such as localization and payload detection in single- and multi-view cases. Furthermore, preliminary experiments demonstrate the promising performance of trained networks on practical data, affirming the dataset’s value in addressing real-world drone challenges using optical sensors. The synthetic dataset is publicly available on GitHub (https://github.com/CARG-uOttawa/Multiview-Air-to-Air-simulated-drone-dataset).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".