GeoPriorclip: a foundational remote sensing vision-language model enhanced with cascaded geographic information priors

Remote sensing vision – language models (RSVLMs) have made notable progress in bridging the semantic gap between satellite imagery and natural language. However, two fundamental limitations persist. First, existing vision – language corpora for remote sensing are constructed using general-purpose models and lack integration of structured geographic priors from authoritative remote sensing resources. Second, current RSVLMs do not explicitly model geometric boundaries or spatial relations, leading to suboptimal image – text alignment. To address these limitations, we introduce GeoPrior, a large-scale tri-modal dataset comprising 828k satellite images, 2.48 M textual descriptions, and aligned rasterized maps. GeoPrior encodes detailed geographic priors – including geometric structures, topological relationships, and semantic attributes – extracted from authoritative vector maps. These priors guide GPT-4o in generating knowledge-rich captions tailored to remote sensing, while the rasterized maps provide an additional modality that captures fine-grained boundary information beyond the expressiveness of natural language. Building upon GeoPrior, we propose GeoPriorCLIP, a vision – language model tailored for remote sensing. Our key technical contribution is the geo-aware cross-modal attention module, which injects map-derived spatial priors into the CLIP image encoder to enhance visual representation with explicit geometric and topological awareness. Extensive experiments on 18 public benchmark datasets across four tasks – zero-shot classification, cross-modal retrieval, semantic localization, and zero-shot semantic segmentation – demonstrate that GeoPriorCLIP consistently outperforms state-of-the-art RSVLMs. The code and data will be released upon acceptance.