The global number of landslides cases has increased due to greater urbanization and land use, as well as higher frequency of extreme climatic events. Since these events have strong social and economic impacts on Brazil and worldwide, they are considered one obstacle for the sustainable development according to the United Nations (UN). Studies conducted to date aim to find landslide prone areas, nevertheless, few studies address automatic methodologies for systematic monitoring of these areas. Thus, the main objective of this study is to use deep learning with convolution neural networks (CNN) in order to automate the segmentation of landslide scars on remote sensing imagery. The automation of this process is relevant to reduce the social and economic impacts since it allows constant monitoring of landslide susceptible areas, moreover, it is important for the validation of predictive models. The methodology that will be used, aiming to achieve the expected results, addresses aspects related to the database, architecture, hyperparameters, validation and result metrics of the model. The CNN will be constructed with the Python libraries: Keras and TensorFlow. (AU)