A Survey on Forest Fire Monitoring Using Unmanned Aerial Vehicles
Bibliographic record
Abstract
Every year, forest fire causes heavy death toll and destruction around the world. The number of forest fires is increasing each year along with the damages associated with it. At this point, traditional forest fire detection methods such as point sensors, thermal sensors, watch tower, human patrol and satellite imagery are not being enough to provide early detection and continuous monitoring. Recent developments in electronics and control systems have made unmanned aerial vehicles (UAVs) more readily available and created an opportunity to utilize them for continuous forest monitoring with higher flexibility, maneuverability and precision. Early level experiments show that the limitations of the previous methods could be overcome by UAV-facilitated forest fire monitoring strategies. This paper highlights the basic idea of UAV-based forest fire monitoring and relevant researches and operations that have been conducted in this field thus far. The future of forest fire monitoring relies more on the use of UAVs and their onboard mission payloads, and the main motivation of this paper is to help for identifying the methodologies behind the existing systems and to find new methods of improving the UAV systems to fight this dreadful calamity.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".