Unmanned Aerial-Ground Systems for Wildfire Detection, Global Localization and Suppression
Bibliographic record
Abstract
This paper presents a new and comprehensive autonomous framework for wildfire detection, global localization, and suppression leveraging Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). This work significantly expands upon existing research focusing solely on UAV-based wildfire mitigation. By synergistically combining the capabilities of UAVs with those of UGVs, our system enhances situational awareness and operational effectiveness. Notably, this approach integrates cutting-edge localization technologies, including the Simultaneous Localization and Mapping (SLAM) system for accurate global mapping of fire spots to enable near-meter-level precision tracking of wildfires even when obscured from view by vegetation or debris. To further enhance accuracy, we employ a motion model and an Iterated Extended Kalman Filter (iEKF) incorporating both Global Navigation Satellite Systems (GNSS) data and semantic vision information. Extensive simulations in cluttered environments confirm the efficacy of our proposed framework in supporting timely and accurate wildfire detection and suppression.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".