RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model

2025-04-07

AI Summary

Current remote sensing vision-language models (VLMs) are constrained by closed-world assumptions and lack of external knowledge, limiting their capacity for complex semantic reasoning requiring domain- or world-level knowledge. To address this, we propose RS-RAG, the first retrieval-augmented generation framework tailored for remote sensing. We introduce RSWK—the first multimodal dataset integrating global landmark knowledge, covering 175 countries and 14,000 landmarks with aligned image-text pairs. Our method comprises: (1) construction of a multimodal vector database; (2) cross-modal retrieval and re-ranking; (3) joint embedding of high-resolution imagery and textual knowledge; (4) VLM fine-tuning; and (5) knowledge-aware prompt engineering. Extensive experiments demonstrate substantial improvements over state-of-the-art methods on image captioning, classification, and visual question answering—validating that external knowledge injection meaningfully enhances semantic reasoning capabilities in remote sensing.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent progress in VLMs has demonstrated impressive capabilities across a variety of tasks in the natural image domain. Motivated by these advancements, the remote sensing community has begun to adopt VLMs for remote sensing vision-language tasks, including scene understanding, image captioning, and visual question answering. However, existing remote sensing VLMs typically rely on closed-set scene understanding and focus on generic scene descriptions, yet lack the ability to incorporate external knowledge. This limitation hinders their capacity for semantic reasoning over complex or context-dependent queries that involve domain-specific or world knowledge. To address these challenges, we first introduced a multimodal Remote Sensing World Knowledge (RSWK) dataset, which comprises high-resolution satellite imagery and detailed textual descriptions for 14,141 well-known landmarks from 175 countries, integrating both remote sensing domain knowledge and broader world knowledge. Building upon this dataset, we proposed a novel Remote Sensing Retrieval-Augmented Generation (RS-RAG) framework, which consists of two key components. The Multi-Modal Knowledge Vector Database Construction module encodes remote sensing imagery and associated textual knowledge into a unified vector space. The Knowledge Retrieval and Response Generation module retrieves and re-ranks relevant knowledge based on image and/or text queries, and incorporates the retrieved content into a knowledge-augmented prompt to guide the VLM in producing contextually grounded responses. We validated the effectiveness of our approach on three representative vision-language tasks, including image captioning, image classification, and visual question answering, where RS-RAG significantly outperformed state-of-the-art baselines.

Problem

• Bridging remote sensing imagery with comprehensive knowledge using multi-modal data
• Enhancing semantic reasoning for complex queries with domain-specific knowledge
• Improving vision-language tasks like captioning and QA via retrieval-augmented generation

Innovation

• Multimodal dataset integrating satellite imagery and text
• Retrieval-Augmented Generation framework for remote sensing
• Knowledge-augmented prompt guiding VLM responses