 This study aims to develop accurate landslide maps using machine learning methods such as artificial neural network, ANN, support vector machines, SVM, random forest, RF, and deep learning convolution neural networks, CNNs. The authors used 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 Razowa district in Nepal. They created 20 different maps using ANN, SVM, RF, and different CNN instantiations and compared their results with extensive fieldwork through a mean intersection over union, MIOU, metric. The study found that the small window size CNN method using spectral information only achieved the best result of 78.26% MIOU. However, the authors concluded that deep learning is still in its infancy and more research is needed to understand the effects of different designs on landslide mapping accuracy. This article was authored by Omid Gorbanzadeh, Thomas Blashk, Khalil Golamnya, and others. We are article.tv, links in the description below.