RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning

Area of Science:

• Earth and Space Sciences
• Computer Science
• Artificial Intelligence

Background:

• Remote sensing (RS) research often uses homogeneous data, limiting applicability.
• Existing vision-language models struggle with diverse RS data and advanced tasks.
• A unified framework is needed for multimodal, multi-platform RS image analysis.

Purpose of the Study:

• To develop a model, RingMo-Agent, capable of handling multimodal and multi-platform RS data.
• To enable perception and reasoning tasks based on textual instructions for RS imagery.
• To create a unified framework for diverse real-world RS applications.

Main Methods:

• Utilized a large-scale dataset (RS-VL3M) with over 3 million image-text pairs across optical, SAR, and IR modalities.
• Implemented modality-adaptive representations using separated embedding layers to reduce cross-modal interference.
• Developed an agent-based framework with external tool use and reinforcement learning for embodied navigation.

Main Results:

• RingMo-Agent demonstrates effectiveness in visual understanding and complex analytical tasks.
• The model shows strong generalizability across different sensing modalities and platforms.
• Experiments confirm the model's capability in perception and reasoning for diverse RS data.

Conclusions:

• RingMo-Agent provides a unified and effective solution for multimodal, multi-platform RS vision-language tasks.
• The model's approach addresses limitations of existing methods in handling data diversity.
• RingMo-Agent advances real-world applications of RS image analysis through enhanced perception and reasoning.