  The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of
interest and research in the field of large language models for Artificial
General Intelligence (AGI). These models provide intelligent solutions close to
human thinking, enabling us to use general artificial intelligence to solve
problems in various applications. However, in remote sensing (RS), the
scientific literature on the implementation of AGI remains relatively scant.
Existing AI-related research in remote sensing primarily focuses on visual
understanding tasks while neglecting the semantic understanding of the objects
and their relationships. This is where vision-language models excel, as they
enable reasoning about images and their associated textual descriptions,
allowing for a deeper understanding of the underlying semantics.
Vision-language models can go beyond visual recognition of RS images, model
semantic relationships, and generate natural language descriptions of the
image. This makes them better suited for tasks requiring visual and textual
understanding, such as image captioning, and visual question answering. This
paper provides a comprehensive review of the research on vision-language models
in remote sensing, summarizing the latest progress, highlighting challenges,
and identifying potential research opportunities.
