As natural disasters are induced by geodynamic activities or abnormal changes in the environment, geological hazards tend to wreak havoc on the environment and human society. Recently, the dramatic increase in the volume of various types of Earth observation ‘big data’ from multiple sources, and the rapid development of deep learning as a state-of-the-art data analysis tool, have enabled novel advances in geological hazard analysis, with the ultimate aim to mitigate the devastation associated with these hazards. Motivated by numerous applications, this paper presents an overview of the advances in the utilization of deep learning for geological hazard analysis. First, six commonly available Earth observation data sources are described, e.g., unmanned aerial vehicles, satellite platforms, and in-situ monitoring systems. Second, the deep learning background and six typical deep learning models are introduced, such as convolutional neural networks and recurrent neural networks. Third, focusing on six typical geological hazards, i.e., landslides, debris flows, rockfalls, avalanches, earthquakes, and volcanoes, the deep learning applications for geological hazard analysis are reviewed, and common application paradigms are summarized. Finally, the challenges and opportunities for the application of deep learning models for geological hazard analysis are highlighted, with the aim to inspire further related research.