# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
###### Abstract
Hence, the 1D representation is obtained from the landslide influencing attributes, and this 1D grid cell is converted first to a 2D matrix and then to a 3D representation to fit each CNN architecture. Thus, there is no guarantee that the data have a spatial relationship that can be exploited for feature extraction by the cross-correlation operation performed by the network. The proposal was evaluated with data from Yanshan County (China), involved 16 landslide influencing factors, and the landslide inventory map had only 400 locations distributed for training and validation. Subsequently, a paper on the application of a deep learning model for the prediction of landslide susceptibility was presented. Although this proposed model relates some hyperparameters and the tool used for its implementation, it does not describe fundamental characteristics such as the dimensions of the filters or the number of layers of the network, which are necessary to replicate the model proposed therein. The proposal was evaluated with data from th Muong Lay district (Vietnam) and involved nine conditioning factors from 217 landslide events and 217 non-landslide samples [26]. Another recently presented work proposes the use of a CNN for feature extraction and the use of a DNN (deep learning network) to classify pixels into high and low susceptibility groups [27]. However, as in the previous case, the model proposal does not provide the necessary details to obtain a complete view of the model or to replicate it. Furthermore, the dataset used to train and validate the network (222 landslides) could be too small to train a deep learning model, as in the previous cases. This work used 16 triggering factors, and it was evaluated with data from the Isfahan province in Iran. In [28], a CNN of three convolutional layers plus two fully connected layers and a classification layer was proposed for landslide susceptibility mapping. The input data used in this study correspond to 11 causative factors of 405 landslides from Wenchuan County (China), which were structured into 30 m resolution raster data. The dimension of the input data to CNN was \(17\times 17\) pixels. Other works have proposed the use of software solutions (Microsoft Power BI) to explore data, analyze root-cause, identify key influences, and identify and explain anomalies through the use of landslide data and artificial intelligence (natural language processing and CNN) [29]. The use of recurrent neural networks (RNN) has also been proposed in recent works. For this purpose, 17 landslide influencing factors were used, each one as a single-band image, where each pixel was converted to a sequential sample from the perspective of importance. Furthermore, all factor layers were stacked together. This proposal was evaluated with data from Yongxin County (China) with 364 historical landslide locations. The proposal tested four RNN-related models: RNN, long short term memory (LSTM), gated recurrent unit (GRU) and simple recurrent unit (SRU) [30]. Of the existing methods in the literature, three important aspects stand out. The first has to do with the lack of clarity in the hyperparameters of some CNN models proposed in the literature, which hinders the replication of the models or their direct application in other geographical areas. The second has to do with the diversity of thematic variables used, which were identified in 596 different input thematic variables and reclassified into 23 classes in [17]. The third aspect has to do with the fact that, in some cases, the number of landslides used to train a model was too small for the extent of the study area to be geomorphologically relevant [17]. Under the above context, this paper presents a new deep learning architecture for landslide detection. The proposed architecture is characterized by its simplicity and by applying both spatial and channel-wise processing since the input samples are taken in the neighborhood around each pixel.