A Novel Transfer Learning based CNN Model for Wildfire Susceptibility Prediction
Bibliographic record
Abstract
Wildfires are one of the most commonly occurring natural disasters in the world, posing significant threats to ecosystems and human settlements alike. One of the most important risk mitigation strategies is to implement early warning systems by identifying the regions more susceptible to wildfires. The development of remote sensing technologies combined with the increasing success of deep learning algorithms has greatly accelerated the development of such systems. Significant research has been done so far for wildfire detection in ground level imagery using neural network classifiers, but there is a lack of research focusing on satellite imagery. This paper proposes a method of wildfire risk assessment of large land regions focusing on remote sensing satellite imagery. The dataset used consists of 31280 satellite images each of size 350 x 350 pixels in jpg format from the Quebec region and was built using wildfire data from Canada's Open Government Portal website to identify regions where wildfires have occurred, and satellite images of those regions before or during occurrence have been used as the wildfire class in the binary classification. We implemented different CNN based classification models, namely VGG16, ResNet50, Xception and InceptionV3 as well as four VGG-16 based transfer learning models. All the models were simulated on 5640 test images and their performance was compared. Our proposed transfer learning model having a three layered pyramid structure with Batch Normalization and Dropouts yielded the best results, with an accuracy of 0.9650, 0.9715 precision and an F-1 score of 0.9648 outperforming all of the traditional as well as the basic transfer learning models by a notable margin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".