###### Abstract
In particular, for urgent landslide susceptibility assessments (e.g., post-earthquake ones), the assessment results can be questionable if the area is unfamiliar to experts. Data-driven methods use machine learning algorithms to analyze the correlation between landslide distribution and causative factors to assess landslide susceptibility. Regional-scale LSM represents a significant leap forward from previous studies, and integrates the Geographic Information System and the modern advent of machine learning (e.g., artificial intelligence). Typical machine learning algorithms include support vector machines [17], logistic regression [22], information value [23], weight of evidence [24], index of entropy [25], frequency ratio [25], artificial neural networks [26], and random forest [27]. Machine learning algorithms are considered more suitable for LSM over large and complex areas than physically-based methods and knowledge-based methods. Data-driven LSM models have their own advantages and disadvantages. These methods are unable to eliminate the differences of causative factors in terms of magnitude and nature, and also have other problems such as low training efficiency and negative influence by several modeling parameters. For example, the spatial relationship between landslide locations and the parameters that affect them cannot be calculated in machine learning method [26; 28; 29]. The model requires a large effort to collect and validate the necessary input data, and model preparation takes a long time. In addition, the performance of the model is vulnerable to the study area and has low generalization; therefore, it is difficult to compare the susceptibility classes from different locations [30]. The means to improve the performance of the LSM model have attracted increasing attention. At present, methods related to improving model performance mainly focus on the following aspects: (1) improving the accuracy of non-landslide sample points selection or the appropriate proportion of sample selection [31; 32]; (2) assigning weights among different causative factors [33; 34; 35; 36]; (3) optimizing the internal parameters of assessment models [37; 38; 39; 40]; (4) assessment by region after delineation of the study area [41]; and (5) integrating different assessment models [9; 42; 43; 44; 45]. In essence, these studies explore and improve the assessment methods or processes, but do not conduct in-depth analysis from the perspectives of the original data from multiple modalities. They link the features from different causative factors directly without considering the possible impact of different data modalities on the performance of LSM. As causative factors of earthquake-induced landslides are various, such as seismology, terrain, geology, hydrology, and human activity, different data modalities reflect different facets of impacts on landslide occurrence and contain complementary information for LSM. Thus, it would be an interesting direction to fuse multiple modal data to further improve the LSM accuracy. In this paper, through treating the data of different types as heterogeneous data, we distinguish them with different modalities, and subsequently fuse the characteristic data at the data modalities level. Thus, the complementary information of different modalities can be fully considered. We develop a new framework for landslide susceptibility assessment by taking into account the different data modalities and using nonlinear fusion to fuse the different modal data simultaneously. After that, we utilize the fused data to perform the landslide susceptibility mapping. To validate the performance of the presented LSM model, we take Wenchuan as the study area and compare different LSM results calculated by the multi-modal classification (MMC) model and three benchmark landslide susceptibility assessment models.