  Artificial Intelligence (AI) technologies have profoundly transformed the
field of remote sensing, revolutionizing data collection, processing, and
analysis. Traditionally reliant on manual interpretation and task-specific
models, remote sensing has been significantly enhanced by the advent of
foundation models--large-scale, pre-trained AI models capable of performing a
wide array of tasks with unprecedented accuracy and efficiency. This paper
provides a comprehensive survey of foundation models in the remote sensing
domain, covering models released between June 2021 and June 2024. We categorize
these models based on their applications in computer vision and domain-specific
tasks, offering insights into their architectures, pre-training datasets, and
methodologies. Through detailed performance comparisons, we highlight emerging
trends and the significant advancements achieved by these foundation models.
Additionally, we discuss the technical challenges, practical implications, and
future research directions, addressing the need for high-quality data,
computational resources, and improved model generalization. Our research also
finds that pre-training methods, particularly self-supervised learning
techniques like contrastive learning and masked autoencoders, significantly
enhance the performance and robustness of foundation models in remote sensing
tasks such as scene classification, object detection, and other applications.
This survey aims to serve as a resource for researchers and practitioners by
providing a panorama of advances and promising pathways for continued
development and application of foundation models in remote sensing.
