 This study aims to develop accurate landslide maps using machine learning methods such as Artificial Neural Network, ANM, Support Vector Machines, SVM, Random Forest, RF, and Deep Learning Convolution Neural Networks, CNNs. The study uses optical data from the rapid-eye satellite and topographic factors to analyze the potential of these methods for landslide detection in the highly landslide-prone Razzua district in Nepal. The results show that a small window size CNN using spectral information only achieves the best accuracy of 78.26% mean intersection over union, MIU. However, additional information from a 5M digital elevation model does not improve the overall classification accuracy. The study concludes that deep learning can improve landslide mapping in the future if the effects of different designs are better understood and enough training samples exist. This article was authored by Omid Gorbanzadeh, Thomas Blashk, Khalil Golamnya, and others. We are article.tv, links in the description below.