Autonomous Leader-Follower UAV System for Real-Time Wildfire Detection and Suppression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wildfires threaten ecosystems and communities and require rapid detection and effective suppression. This paper presents an autonomous leader-follower UAV system for real-time wildfire detection and suppression. A leader UAV employs deep learning and thermal imaging to detect fires accurately, even under low-visibility conditions, achieving 95.2% detection accuracy. It integrates visual and thermal data to refine fire coordinates, which are relayed to a ground station. The ground station directs a follower UAV, equipped with a dual-tank water payload, to execute targeted water drops with 0.2 m precision along optimized suppression paths. By separating detection and suppression roles, the system enhances mission endurance and payload capacity. This coordinated approach enables rapid and accurate wildfire containment, offering a scalable solution for early-stage fire management.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it