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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

artificial intelligence, remote sensing, remote sensing foundation model, physics-informed deep learning, intelligent interpretation, onboard processing, Earth observation

Abstract

Remote sensing science and technology, as a key discipline for Earth observation and global change research, faces systemic challenges in processing massive multi-source data, accurately extracting complex information, and delivering high-timeliness application services. The rapid advances in artificial intelligence (AI) provide a new opportunity to address the deep-seated dilemma in remote sensing of being “data-rich but insufficient in effective information mining”. Guided by a problem-oriented approach, this study first systematically analyzes the core challenges facing the development of remote sensing across four dimensions: data understanding, technical methods, scientific mechanisms, and application ecosystems. It then reviews the technical evolution of AI-empowered remote sensing, with a focus on four frontier paradigms — self-supervised and weakly supervised learning, multi-modal and cross-modal fusion, physics-informed deep learning, and remote sensing foundation models and world models, and conducts an in-depth analysis of their innovative value and implementation pathways in three representative scenarios: smart agriculture, disaster emergency response, and ecological security. Finally, a systematic strategic layout is proposed from two dimensions of fundamental research and core technology breakthroughs, covering three fundamental scientific directions, such as multi-modal holographic perception theory, multi-sphere intelligent cognition, and bio-inspired remote sensing intelligent models, as well as four core technology directions: the construction of standardized remote sensing sample libraries, multi-source collaborative observation, full-chain autonomous intelligence, and onboard spaceborne computing. Building on these, the study assesses the global competitive landscape of “AI+remote sensing” and China’s position, arguing that while maintaining independent and controllable development, China should strengthen its engagement in open-source ecosystems and the infrastructuralization of remote sensing capabilities. By building a technology chain of “intelligent perception - cognitive understanding - decision support”, this study advances remote sensing from traditional observational analysis toward an intelligent and systematic direction, providing important support for enhancing China’s earth observation science and technology innovation capability and serving national strategic needs.

First page

1182

Last Page

1192

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

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