# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
###### Abstract  
In this work, a new convolutional neural network architecture is proposed to evaluate the susceptibility to landslides. It is a supervised learning algorithm that has been trained from data whose labels have been obtained by applying a heuristic method that involves geological, geomorphological and land use information. The attributes contemplated the use of multispectral data and spectral indices, in addition to slope and DEM data. Although the cartographic unit in the proposed method is the pixel, the processing was performed at the patch level since it involved the use of spatial information around each pixel. Therefore, the proposed deep learning architecture is characterized by its simplicity and by applying both spatial and channel processing. The proposed method presents similar performance to state-of-the-art methods, achieving an F1 score higher than 88% on test data with low computational cost and pixel-level accuracy. landslide; susceptibility; deep learning; CNN; GIS +
Footnote †: journal: Applied  
traditional techniques of visual interpretation and digital analysis of aerial photographs and satellite images [4; 5; 6], there are supervised classification methods to differentiate hill-slope landslides from other landcover units [7], and the availability of high-resolution images (including fused images) has become important [8; 9]. It is also possible to combine remote sensing-based interpretations with heuristic and statistical susceptibility models [10] and to evaluate the effects of image features with respect to maps produced from different methods [11]. For example, in [12], earth observation data were used to map landslide risk by compiling regional-scale landslide inventory maps using satellite photo-interpretation and calculating displacement rates quantified from satellite SAR (synthetic-aperture radar) interferometry. Similarly, some alternatives have included geomorphological inventories of seasonal and multi-temporal events, and they have used other technologies related to high-resolution digital elevation models (DEM) [13], as in the analysis of the influence of tectonics on the progressive erosion of landscapes and their classification from the combination of DEM-based geomorphic indices [14]. Meanwhile, other proposed methodologies include typical morphometric parameters derived from DEM using \"window-based\" morphometric indices as basic predictor variables for the landslide susceptibility assessment [15]. For example, sixteen morphometric factors derived from a DEM using ASTER (Advanced Spaceborne Thermal Spaceborne Emission and Reflection Radiometer) data and other geological and environmental predictors were recently used [16]. Regarding the methods for modeling the susceptibility to landslides on a statistical basis, a critical review of the methods proposed in the last 25 years was presented in [17]. According to this study, different categories of models related to geographic area, type of landslide, type of inventory and period covered, statistical model, evaluation method, or uncertainty evaluation were identified. The study also concluded that the most common statistical methods included logistic regression, neural networks, data overlay, index-based analysis, and the trend towards machine learning-based methods, which has been corroborated in recent years. In addition, complementary studies have addressed the evaluation and comparison of traditional methods such as support vector machines [18], random forest [19], or ensemble and hybrid algorithms [20; 21; 22; 23]. Regarding the use of deep learning methods, recent work has compared different neural network architectures while proposing new ones [24; 25]. For instance, the authors in [25] propose the use of Convolutional Neural Networks (CNN) of two layers for landslide susceptibility mapping using three data representation forms (i.e., 1D, 2D or 3D).