# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 3 Materials and Methods
### LW-FIRE: A Lightweight CNN for Wildfire Image Classification
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**No.** & **Block Type** & **Block Description** & **Size of Feature Maps** & **No. Parameters** \\ \hline
1 & Conv2D (ReLU) & 2D convolution layer & (150, 150, 32) & 896 \\ \hline
2 & BatchNormalization & Batch normalization layer & (75, 75, 32) & 128 \\ \hline
3 & MaxPooling2D & Max pooling for 2D data & (75, 75, 32) & 0 \\ \hline
4 & Conv2D (ReLU) & 2D convolution layer & (75, 75, 64) & 18,496 \\ \hline
5 & BatchNormalization & Batch normalization layer & (75, 75, 64) & 256 \\ \hline
6 & MaxPooling2D & Max pooling for 2D data & (37, 37, 64) & 0 \\ \hline
7 & Conv2D (ReLU) & 2D convolution layer & (37, 37, 128) & 73,856 \\ \hline
8 & BatchNormalization & Batch normalization layer & (37, 37, 128) & 512 \\ \hline
9 & MaxPooling2D & Max pooling for 2D data & (18, 18, 128) & 0 \\ \hline
10 & Conv2D (ReLU) & 2D convolution layer & (18, 18, 128) & 147,584 \\ \hline
11 & BatchNormalization & Batch normalization layer & (18, 18, 128) & 512 \\ \hline
12 & MaxPooling2D & Max pooling for 2D data & (9, 9, 128) & 0 \\ \hline
13 & Conv2D (ReLU) & 2D convolution layer & (9, 9, 128) & 147,584 \\ \hline
14 & BatchNormalization & Batch normalization layer & (9, 9, 128) & 512 \\ \hline
15 & Conv2D (ReLU) & 2D convolution layer & (9, 9, 128) & 147,584 \\ \hline
16 & BatchNormalization & Batch normalization layer & (9, 9, 128) & 512 \\ \hline
17 & MaxPooling2D & Max pooling for 2D data & (4, 4, 128) & 0 \\ \hline
18 & Flatten & Flatten the input & 2048 & 0 \\ \hline
19 & Dense (ReLU) & Densely connected NN layer & 256 & 524,544 \\ \hline
20 & Dropout & Dropout regularization with rate 0.6 & 256 & 0 \\ \hline
21 & Dense (ReLU) & Densely connected NN layer & 128 & 32,896 \\ \hline
22 & Dropout & Dropout regularization with rate 0.3 & 128 & 0 \\ \hline
23 & Dense (ReLU) & Densely connected NN layer & 64 & 8256 \\ \hline
24 & Dropout & Dropout regularization with rate 0.15 & 64 & 0 \\ \hline
25 & Dense (ReLU) & Densely connected NN layer & 32 & 2080 \\ \hline
26 & Dropout & Dropout regularization with rate 0.075 & 32 & 0 \\ \hline
27 & Dense (ReLU) & Densely connected NN layer & 16 & 528 \\ \hline
28 & Dropout & Dropout regularization with rate 0.0325 & 16 & 0 \\ \hline
29 & Dense (Sigmoid) & Densely connected NN layer & 1 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the LW-FIRE150 deep convolutional neural network architecture. significantly reduces the time for model convergence. The max-pooling layer is used after the convolution layers to reduce the spatial dimensionality of the output feature maps. A batch normalization (BN) layer was added after every convolution layer to normalize the values in the batch of images and to encourage stable estimation. This technique reduces the overfitting problem [26]. Finally, we introduced several dense layers with dropout regularization techniques that randomly set hidden units to 0 with different frequency ratings. The dropout layer is applied only during the training stage.