  Vision-Language Foundation Models (VLFMs) have made remarkable progress on
various multimodal tasks, such as image captioning, image-text retrieval,
visual question answering, and visual grounding. However, most methods rely on
training with general image datasets, and the lack of geospatial data leads to
poor performance on earth observation. Numerous geospatial image-text pair
datasets and VLFMs fine-tuned on them have been proposed recently. These new
approaches aim to leverage large-scale, multimodal geospatial data to build
versatile intelligent models with diverse geo-perceptive capabilities, which we
refer to as Vision-Language Geo-Foundation Models (VLGFMs). This paper
thoroughly reviews VLGFMs, summarizing and analyzing recent developments in the
field. In particular, we introduce the background and motivation behind the
rise of VLGFMs, highlighting their unique research significance. Then, we
systematically summarize the core technologies employed in VLGFMs, including
data construction, model architectures, and applications of various multimodal
geospatial tasks. Finally, we conclude with insights, issues, and discussions
regarding future research directions. To the best of our knowledge, this is the
first comprehensive literature review of VLGFMs. We keep tracing related works
at https://github.com/zytx121/Awesome-VLGFM.
