AI large models, with their advantages in generalization, universality, and high accuracy, have become the cornerstone of various AI applications such as computer vision, natural language processing. Based on the analysis of the development process, value, and challenges of AI large models, this article first discusses the research progress of remote sensing large models from three perspectives: data, model, and downstream tasks. At the data level, there is a transition from single modality to multi-modality; at the model level, there is a shift from small models to large models; and at the downstream task level, there is a development from single-task to multi-task. Next, the article explores three key development directions for remote sensing large models: multi-modal remote sensing large models, interpretable remote sensing large models, and reinforcement learning from human feedback(RLHF). Furthermore, it realizes a construction approach for remote sensing large models, namely “construction of unlabeled dataset-self-supervised model learning-downstream transfer application”. Technical experiments have been conducted to validate the significant advantages of remote sensing large models. Finally, the article concludes and provides prospects, emphasizing the need to focus on application tasks and combine theoretical methods, engineering technology, and iterative applications to achieve low-cost training, efficient and fast inference, lightweight deployment, and engineering-based applications for remote sensing large models.
