Table 7.

A combination of segmentation and classification-based methods for forest fire monitoring and surveillance.

Authors	Year	Method	Architecture	Accuracy	Application	Augmentation	Type of Data
Zhao et al. [11]	2018	DCNN	Deep CNN - Saliency	Accuracy - 98%	Segmentation and Classification	Yes	Aerial, UAV, Satellite and Ordinary View Image
Khryashchev and Larionov [42]	2020	CNN	U-Net & ResNet34	F1-score - 0.465	Segmentation and Classification	Yes	Satellite Image
Ciprián-Sánchez et al. [59]	2021	CNN	U-Net, FusionNet & VGG-16	F1-score - 0.9263	Segmentation and Classification	Yes	Image
Ghali et al. [77]	2022	DCNN	TransU-Net, TransFire, EfficientSeg, EfficientNet-B5 and DenseNet-201	Accuracy - 99.9%	Segmentation and Detection	Yes	Image