# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 5 Discussion  
In order to compare the proposed algorithm with some state-of-the-art approaches, it must be taken into account that the number of susceptibility classes or levels in recent studies is not unified since different studies have been carried out that consider two, three or five levels, for example. In this regard, for comparison purposes, a recent study was selected that also worked with two levels of susceptibility and evaluated different machine learning techniques [27]. Therefore, some methods from this study were selected, including results from a proposal based on a deep convolutional neural network (see Table 2). From the comparative study, the scheme based on Multi Layer Perceptron (MLP), the scheme based on Decision Tree and the proposed method in that paper were selected (based on deep convolutional neural network) [27]. Although that paper refers to the list of hyperparameters of the compared models, it does not clarify which values were finally selected. Even in the model proposed in that paper, hyperparameters such as the number and type of hidden layers or the number of units and filters are not given. It is  
Figure 11: Confusion matrix for the test set (13,795 samples). The positive class corresponds to high susceptibility. also important to note that this predictive model was applied in the evaluation of landslide susceptibility in the Isfahan Province, Iran. According to Table 2, the accuracy and the F1-score of the proposed method outperforms the score obtained by the machine learning and deep learning methods reported in [27]. Although the data used for the evaluation of the proposed method and the methods presented in [27] are different, something important to consider in this comparison has to do with the size of the test dataset used, as well as the balance of the classes in it. In particular, the use of metrics such as F1-score in unbalanced classes may bias the result, depending on the distribution of the classes. The results reported in [27] were calculated using a test dataset of 44 samples, whereas the size of the test dataset in the proposed method was 13,795 samples. Regarding the labeling and input data size (image patch), it is important to consider whether the evaluation was performed at the pixel level or on a per-patch basis. Although the CNN-DNN method reported in [27] speaks of a patch analysis, the training data speak of landslide locations (i.e., points). In the proposed method, the input training data (features) are constructed from an MS image extracting patches with a size of \(5\times 5\) around each pixel. Therefore, the total number of examples corresponds to the same number of pixels in the image since the neighborhood of the pixel with a stride 1 is taken. Hence, the data are labeled at the pixel level. The importance of this aspect lies in the requirements imposed on the classifier since pixel-level labeling is more demanding than patch-level labeling (where the label is averaged over the entire patch). From the network complexity point of view, the first piece of information is the number of convolutional layers in the network (see Table 2). Beyond this, it is important to consider other aspects, such as the size of the convolution filters or the number of units in fully connected layers. This allows calculating the total number of network parameters (trainable and non-trainable), which are associated with the size (MB) of the architecture. For the case of the proposed method, the network has only 8972 parameters, of which 8920 are trainable. This small number of parameters is mainly due to the small size of the input data and the depth of the network and is significantly smaller compared to the size of standard deep learning architectures that can have millions of parameters. Finally, it is also necessary to consider the number of input data channels used in the schemes (which is equivalent to the number of thematic variables in the proposed method) since this input influences the computational cost of the architecture.