Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges
Haifeng Li, Wang Guo, Haiyang Wu, Mengwei Wu, Jipeng Zhang, Qing Zhu, Yu Liu, Xin Huang, Chao Tao
TL;DR
This paper identifies the limitations of vision-centered remote sensing interpretation in semantic abstraction and interactive decision-making, and proposes a language-centered paradigm anchored in Global Workspace Theory. It positions LLMs as a universal cognitive hub that unifies perception, knowledge, tasks, and actions to enable integrated understanding, reasoning, and autonomous decision-making in geospatial analysis. The work outlines three core technical challenges—unified multimodal representation, knowledge association and reasoning, and decision-making/execution—and surveys architectural strategies including explicit/implicit semantic alignment, task understanding, and knowledge retrieval, alongside training datasets and evaluation benchmarks. It also discusses the emergence of agent-based architectures and outlines future directions focused on adaptive multimodal alignment, dynamic spatiotemporal reasoning, trustworthy inference, and autonomous interactive interpretation agents. Overall, the paper provides a roadmap toward cognition-driven, open-world remote sensing analysis with potential for substantial practical impact in geospatial intelligence.
Abstract
The mainstream paradigm of remote sensing image interpretation has long been dominated by vision-centered models, which rely on visual features for semantic understanding. However, these models face inherent limitations in handling multi-modal reasoning, semantic abstraction, and interactive decision-making. While recent advances have introduced Large Language Models (LLMs) into remote sensing workflows, existing studies primarily focus on downstream applications, lacking a unified theoretical framework that explains the cognitive role of language. This review advocates a paradigm shift from vision-centered to language-centered remote sensing interpretation. Drawing inspiration from the Global Workspace Theory (GWT) of human cognition, We propose a language-centered framework for remote sensing interpretation that treats LLMs as the cognitive central hub integrating perceptual, task, knowledge and action spaces to enable unified understanding, reasoning, and decision-making. We first explore the potential of LLMs as the central cognitive component in remote sensing interpretation, and then summarize core technical challenges, including unified multimodal representation, knowledge association, and reasoning and decision-making. Furthermore, we construct a global workspace-driven interpretation mechanism and review how language-centered solutions address each challenge. Finally, we outline future research directions from four perspectives: adaptive alignment of multimodal data, task understanding under dynamic knowledge constraints, trustworthy reasoning, and autonomous interaction. This work aims to provide a conceptual foundation for the next generation of remote sensing interpretation systems and establish a roadmap toward cognition-driven intelligent geospatial analysis.