AI-Enabled Cislunar Space Situational Awareness

Cislunar space, the region between Earth and the Moon, is becoming an increasingly active and strategically significant domain for scientific exploration, commercial activities, and national security. Over the next decade, more than 400 spacecraft are expected to operate in this region, accompanied by a growing volume of space debris. Ensuring effective situational awareness in cislunar space is essential for collision avoidance, mission safety, and the protection of critical assets. However, the unique challenges of this environment, such as complex multi-body dynamics, limited observational coverage, and unpredictable space traffic patterns, necessitate advanced sensing, tracking, and predictive modeling solutions.

Artificial intelligence (AI) is transforming space situational awareness by enabling the automated processing and interpretation of vast and diverse datasets, including remote sensing imagery, orbital dynamics, and space environment parameters. Cutting-edge AI techniques, such as deep learning, reinforcement learning, computer vision, and natural language processing, enhance target detection, continuous tracking, and behavior prediction for space objects. Additionally, the emergence of lightweight large-scale models, such as DeepSeek, has introduced new opportunities for real-time data analysis, autonomous decision-making, and scalable cislunar surveillance. These advancements promise to significantly improve the accuracy, efficiency, and adaptability of space situational awareness frameworks.

Scope and Topics of Interest:

This special issue aims to explore the role of AI in addressing key challenges in cislunar space situational awareness, with a focus on data-driven intelligence, predictive analytics, and autonomous monitoring. We welcome original research and review papers on topics including, but not limited to:

• AI-driven situational awareness systems for real-time cislunar space monitoring and risk assessment.
• High-precision three-body dynamics modeling tailored for spacecraft navigation and space traffic management.
• AI-enhanced orbital cataloging and classification of cislunar objects in complex gravitational environments.
• AI-based detection, recognition, and tracking of dim and small objects in cislunar space.

Submission Details:

The journal’s submission platform (Editorial Manager) will open for submissions to this special issue on February 17, 2025. Authors should prepare their manuscripts following the Guide for Authors and select the article type “VSI: AI-Enabled Cislunar Space Situational Awareness" when submitting online.

The Guide for Authors and submission portal can be found on the Journal Homepage: https://www.keaipublishing.com/en/journals/space-habitation.

The Submission Website is available at: https://www.editorialmanager.com/spaceh/default.aspx.

Important Dates:

Submission Deadline: March 31, 2025 

Peer Review Completion: April 30, 2025 

Decision Notification: June 10, 2025 

Special Issue Editors:

Prof. Jihao Yin, Beihang University

Prof. Gengxin Xie, Chongqing University

Prof. Peng Wang, China Spacesat Co., Ltd.

Prof. Xiaojun Jiang, National Astronomical Observatories, CAS

Assoc. Prof. Dan Qiu, Chongqing University

Assoc. Prof. Yu Zhang, Beihang University

We warmly invite experts and scholars in the relevant research fields to submit original research papers and reviews, and kindly ask you to pay attention to the publication of this special issue and related follow-up activities.