Remote Sensing Agent: Reshaping the Paradigm of Remote Sensing Information Processing.
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| Title: | Remote Sensing Agent: Reshaping the Paradigm of Remote Sensing Information Processing. |
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| Authors: | Liu, Peng1,2 (AUTHOR) liupeng202303@aircas.ac.cn, Zhuang, Rongkai1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1980. 9p. |
| Subjects: | Remote sensing, Information processing, Space perception, Closed loop systems, Multiagent systems |
| Abstract: | Highlights: What are the main findings? We define a customized "4+1" core characteristic framework for Remote Sensing Agent tailored to geospatial Earth observation. Three paradigm shifts of remote sensing processing are systematically summarized from initiation, execution and evaluation dimensions. What are the implications of the main findings? RS Agent will grow into a groundbreaking driving force in the era of geospatial intelligence, contributing to Earth observation and sustainable development. Promising technical routes toward dynamic geoscience knowledge evolution and multi-agent coordination are outlined for subsequent RS Agent development. In the ongoing data-rich era, intelligent cognition is playing an increasingly important role in advancing remote sensing applications. However, traditional intelligent methods for remote sensing processing no longer fully meet the growing demands of this era and still suffer from several limitations, such as passive data-dependent processing, predefined-task execution, and lack of closed-loop optimization. As a customized GeoAI innovation for remote sensing, Remote Sensing Agent has entered an early stage of research explosion. This paper focuses on its paradigm-shifting role in reshaping remote sensing information processing, clarifies the "4+1" core characteristics including multimodal spatial perception, goal-driven spatial mission planning, geoscientific knowledge reference, geospatial workflow execution, and feedback loop. It elaborates the threefold reshaping of remote sensing information processing from initiation mode, execution mode, and evaluation criterion, namely shifting from passive data processing to active task-driven, from predefined-task processing to multi-agent collaboration, and from result-oriented output to full-process closed-loop optimization. Future prospects of Remote Sensing Agent in geoscientific knowledge base optimization, multi-agent collaboration efficiency, and complex-scenario adaptability are discussed. This paper provides targeted and forward-looking perspectives for intelligent innovation research in remote sensing. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We define a customized "4+1" core characteristic framework for Remote Sensing Agent tailored to geospatial Earth observation. Three paradigm shifts of remote sensing processing are systematically summarized from initiation, execution and evaluation dimensions. What are the implications of the main findings? RS Agent will grow into a groundbreaking driving force in the era of geospatial intelligence, contributing to Earth observation and sustainable development. Promising technical routes toward dynamic geoscience knowledge evolution and multi-agent coordination are outlined for subsequent RS Agent development. In the ongoing data-rich era, intelligent cognition is playing an increasingly important role in advancing remote sensing applications. However, traditional intelligent methods for remote sensing processing no longer fully meet the growing demands of this era and still suffer from several limitations, such as passive data-dependent processing, predefined-task execution, and lack of closed-loop optimization. As a customized GeoAI innovation for remote sensing, Remote Sensing Agent has entered an early stage of research explosion. This paper focuses on its paradigm-shifting role in reshaping remote sensing information processing, clarifies the "4+1" core characteristics including multimodal spatial perception, goal-driven spatial mission planning, geoscientific knowledge reference, geospatial workflow execution, and feedback loop. It elaborates the threefold reshaping of remote sensing information processing from initiation mode, execution mode, and evaluation criterion, namely shifting from passive data processing to active task-driven, from predefined-task processing to multi-agent collaboration, and from result-oriented output to full-process closed-loop optimization. Future prospects of Remote Sensing Agent in geoscientific knowledge base optimization, multi-agent collaboration efficiency, and complex-scenario adaptability are discussed. This paper provides targeted and forward-looking perspectives for intelligent innovation research in remote sensing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121980 |
