Ground based cloud images classification based on machine learning is the key technology of photovoltaic power prediction.This technology mainly improves the accuracy of ground based cloud images classification by extracting features from ground based cloud maps through traditional machine learning and deep learning methods.In this paper,the ground based cloud images classification standard and cloud images collection equipment are summarized,the ground based cloud images classification datasets are briefly introduced,and the typical ground based cloud images classification methods are discussed in detail from two aspects of traditional machine learning and depth learning.Then,the performance of different methods on Kiel F and MGCN ground based cloud images classification datasets is compared.Finally,a summary of existing methods for classification of ground based cloud maps is presented,and an outlook is given for the current challenges of ground based cloud map classification.