Leveraging UAVs to Enable Dynamic and Smart Aerial Infrastructure for ITS and Smart Cities: An Overview
Bibliographic record
Abstract
Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as an emerging technology offering a plethora of applications touching various aspects of our lives, such as surveillance, agriculture, entertainment, and intelligent transportation systems (ITS). Furthermore, due to their low cost and ability to be fitted with transmitters, cameras, and other on-board sensors, UAVs can be seen as potential flying Internet-of-things (IoT) devices interconnecting with their environment and allowing for more mobile flexibility in the network. This paper overviews the beneficial applications that UAVs can offer to smart cities, and particularly to ITS, while highlighting the main challenges that can be encountered. Afterward, it proposes several potential solutions to organize the operation of UAV swarms, while addressing one of their main issues: their battery-limited capacity. Finally, open research areas that should be undertaken to strengthen the case for UAVs to become part of the smart infrastructure for futuristic cities are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".