Early Wildfire Detection with UAVs using a Frame Difference Method
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 recent years, communities around the world have witnessed devastating effects of natural disasters caused by climate change. One of the major natural disasters are intensified wildfires, occuring in many regions in the world, leading to severe damages. To mitigate the impacts of wildfires, early detection is a key. Unmanned Aerial Vehicles (UAVs) are a fairly new solution for early wildfire detection. In comparison to traditional methods, hey are relatively cheap and provide higher resolution images. However, the computational resources onboard are limited, which presents challenges for running large image-based deep learning models. In this paper, we propose an effective and efficient approach to detect wildfires from data collected by UAVs, leveraging granular computing. Specifically, we have developed a frame difference method which aims to support the execution of the deep learning based wildfire detection models in resource and power constrained environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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