Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges

TL;DR

This paper proposes a language-centered framework for remote sensing image interpretation inspired by human cognition theories, emphasizing the integration of language models as central reasoning hubs to overcome vision-centered limitations.

Contribution

It introduces a unified theoretical framework based on Global Workspace Theory, positioning LLMs as core cognitive components for remote sensing interpretation.

Findings

Highlights the potential of LLMs as cognitive hubs in remote sensing

Identifies key technical challenges like multimodal representation and reasoning

Proposes a global workspace-driven interpretation mechanism

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.