Remote sensing image scene classification has a wide scope of applications and hence has been receiving remarkable attention. Nowadays, Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. This paper first provides a comprehensive review of the recent progress in remote sensing applications. The existing remote-sensing classiﬁcation methods are categorized into four main categories according to the features they use: manually feature-based methods, unsupervised feature learning methods, supervised feature learning methods, and object based methods. This article then focuses on evaluating the available and public remote-sensing datasets. Finally, a conclusion regarding the current state of art methods and directions for future research are presented.