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An efficient lightweight forest fire risk prediction algorithm is proposed for extracting fire risk features from remotely sensed images for fast and accurate prediction of forest fire risk. Firstly, a lightweight convolutional attention module is introduced to improve the lightweight bottleneck convolutional kernel in the main module of Efficientnet-B0; then the convolutional layers in the network are optimized by a global non-local convolution module to reduce the parameters and computation. On the test set consisting of 88,061 remote sensing images of forest scenes labeled as zero, low, medium and high forest fire risk, the recognition accuracy of the proposed method was 78.38%, an increase of 4.36% over the original one; it was 4.99%, 7.82%, 3.57%, 5.97%, 4.96% and 7.93% higher than that of similar classic neural networks VGG16, ResNet50, DenseNet121, ConvNeXt, MoblieNetV1 and EfficentNetV2, respectively. The model parameter volume of the proposed method is 4.4M, which is 0.9M less than the selected backbone network EfficientNet- B0, compared with others, its parameter volume is less than only 44.10M, 21.16M, 3.58M, 84.6M, and 17.05M. The results show that the proposed method demonstrates the ability to be accurate and fast in forest fire prediction, while the prediction model has lightweight and fewer network parameters.
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