Emerging Urban Challenge: RPAS/UAVs in Cities
Bibliographic record
Abstract
An emerging urban challenge will be the proliferation of Remotely Piloted Aircraft Systems (RPAS) or drones, as their usage grows and drones fill the urban skies. Urban airspace will include many more Uninhabited Aerial Vehicles (UAVs), and related accidents and mishaps will also increase. Applications of UAVs in urban environments include photography and film-making, security monitoring, real estate, construction, property and infrastructure inspections, leisure, public safety (fires, natural disasters, investigations), traffic, and much more. Emerging UAV applications include last-mile drone delivery services. This paper discusses urban scenarios where drones are more ubiquitous. Monitoring and related safety of these UAVs will be increasingly important. Though multi-modal purpose-built drone tracking and monitoring systems will be the most effective solution for detection and tracking of these RPAS, during the transition to more regular drone use in urban areas and in everyday urban applications, a more rapid-deployment and agile detection system is needed that does not require installation of hardware. In this paper, sensory substitution is presented as an approach to use ambient and pre-existing microphones and AI techniques to detect the presence of RPAS. Preliminary results show promise for this agile IoT method as a key solution to this emerging urban challenge.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".