Towards Vision-Language Geo-Foundation Model: A Survey

Understand

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).