CT-Fire: a CNN-Transformer for wildfire classification on ground and aerial images

ABSTRACT

Wildfires pose a serious threat to the environment, ecosystems, property, biodiversity, and human life. Early detection of wildland fires is crucial for effective firefighting and mitigation. In this paper, we propose an ensemble learning method, called CT-Fire, which combines the deep CNN RegNetY and the vision transformer EfficientFormer v2 to recognize and detect forest fires on ground and aerial images. Testing results showed that CT-Fire achieved excellent performance with fast speed and accuracy of 99.62% and 87.77% using ground and aerial images, respectively. CT-Fire also outperformed benchmark CNNs and vision transformer methods, showing its accurate reliability in detecting wildfires. It also surpassed various challenges, including the detection of very small wildfires, background complexity, image quality, and wildland fire variability in terms of intensity, size, and shape.

KEYWORDS:

• Wildfire detection
• CT-Fire
• aerial images
• ground images
• deep learning
• vision transformer
• CNN

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

This work uses four publicly available datasets, FLAME, CorsicanFire, DeepFire, and FIRE, see reference SHAMSOS21,flameLoc,Corsican, corsicanLoc,deepfire,DeepFir2e,Firedataset for data availability. More details about these datasets are available under Section 3.2.

Additional information

Funding

This research was enabled in part by support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number RGPIN-2018-06233.