Using neural networks to detect fire from overhead images

HIGHLIGHTS

SUMMARY

Utilising neural_networks (NN) for active fire detection has proved to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Despite the promising results of using neural_networks, deep learning is still a relatively young field where larger datasets are uncommon. Larger neural_networks such as AlexNet and ResNet often produce accuracies around the 80% mark, other networks like GoogLeNet can reach accuracies around 95%. Commonly, a neural_network model is closely related to the dataset it is being trained on. In Majid et_al propose an . . .

 



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