Dynamic Mapping of Forest Fire Fronts Using Multiple Unmanned Aerial Vehicles
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
In this paper, an algorithm is presented for dynamic mapping of the propagation of forest res using temperature measurements from a squad of 4 UAVs moving in formation. The re front is modeled as a dynamic level curve of the time-varying temperature eld. The temperature and gradient values along the trajectory of the formation centroid are estimated using a Kalman lter framework. The proposed algorithm includes an online trajectory planner for the formation centroid to track the dynamic re front motion. In comparison to vision-based techniques for forest re front mapping, the proposed temperature-based approach to forest re front mapping addresses the dynamic nature of forest re spread by providing additional local information about the unknown time-varying temperature eld, namely the temperature, gradient, Hessian and curvature along the re front curve which can be used to construct time-varying quadratic approximations of the temperature eld. The feasibility of our approach to dynamic forest re front mapping is demonstrated in simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it