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

2025-07-28

AI Summary

Current remote sensing vision-language models suffer from data homogeneity and task singularity, limiting their ability to jointly process heterogeneous multi-source remote sensing imagery and perform complex spatial analysis. To address this, we propose the first unified remote sensing foundation model, which enables joint modeling of cross-platform, cross-modal, long-horizon spatial tasks via three core innovations: (1) modality-adaptive representation learning, (2) task-specific token design, and (3) a token-based high-dimensional latent-state decoding architecture. The model employs a disentangled embedding layer and is pretrained at scale on RS-VL3M—a dataset comprising over 3 million image-text pairs—enabling vision-language pretraining, multimodal alignment, and instruction-driven decoding. Extensive experiments demonstrate that our model significantly outperforms existing methods across diverse remote sensing vision-language benchmarks, exhibiting strong generalization, cross-platform robustness, and advanced capabilities in semantic reasoning and spatial analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Remote sensing (RS) images from multiple modalities and platforms exhibit diverse details due to differences in sensor characteristics and imaging perspectives. Existing vision-language research in RS largely relies on relatively homogeneous data sources. Moreover, they still remain limited to conventional visual perception tasks such as classification or captioning. As a result, these methods fail to serve as a unified and standalone framework capable of effectively handling RS imagery from diverse sources in real-world applications. To address these issues, we propose RingMo-Agent, a model designed to handle multi-modal and multi-platform data that performs perception and reasoning tasks based on user textual instructions. Compared with existing models, RingMo-Agent 1) is supported by a large-scale vision-language dataset named RS-VL3M, comprising over 3 million image-text pairs, spanning optical, SAR, and infrared (IR) modalities collected from both satellite and UAV platforms, covering perception and challenging reasoning tasks; 2) learns modality adaptive representations by incorporating separated embedding layers to construct isolated features for heterogeneous modalities and reduce cross-modal interference; 3) unifies task modeling by introducing task-specific tokens and employing a token-based high-dimensional hidden state decoding mechanism designed for long-horizon spatial tasks. Extensive experiments on various RS vision-language tasks demonstrate that RingMo-Agent not only proves effective in both visual understanding and sophisticated analytical tasks, but also exhibits strong generalizability across different platforms and sensing modalities.

Problem

• Handles multi-modal and multi-platform remote sensing data
• Performs perception and reasoning tasks via user instructions
• Addresses limitations of homogeneous data sources in RS

Innovation

• Large-scale multi-modal dataset RS-VL3M
• Modality adaptive embedding layers
• Task-specific token decoding mechanism