BoucaNet: A CNN-Transformer for Smoke Recognition on Remote Sensing Satellite Images
Bibliographic record
Abstract
Fire accidents cause alarming damage. They result in the loss of human lives, damage to property, and significant financial losses. Early fire ignition detection systems, particularly smoke detection systems, play a crucial role in enabling effective firefighting efforts. In this paper, a novel DL (Deep Learning) method, namely BoucaNet, is introduced for recognizing smoke on satellite images while addressing the associated challenging limitations. BoucaNet combines the strengths of the deep CNN EfficientNet v2 and the vision transformer EfficientFormer v2 for identifying smoke, cloud, haze, dust, land, and seaside classes. Extensive results demonstrate that BoucaNet achieved high performance, with an accuracy of 93.67%, an F1-score of 93.64%, and an inference time of 0.16 seconds compared with baseline methods. BoucaNet also showed a robust ability to overcome challenges, including complex backgrounds; detecting small smoke zones; handling varying smoke features such as size, shape, and color; and handling visual similarities between smoke, clouds, dust, and haze.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".