Advances in the Study of Intelligent Aerospace

Dear Colleagues,

Remote sensing is undergoing a profound transformation, extending beyond traditional methodologies to incorporate a diverse array of modern approaches. The integration of artificial intelligence (AI) into space remote sensing technology has been a pivotal driver for this transformation, heralding a new era of innovation and efficiency. Since the late 1970s, AI has been deployed to automate routine tasks and enhance navigational systems. In contemporary applications, AI confers numerous benefits, including heightened efficiency, enhanced safety, and the capacity to process vast datasets expeditiously. Within the realm of remote sensing and space operation, AI is revolutionizing applications such as predictive maintenance, autonomous navigation, and satellite data analysis, thereby significantly enhancing capabilities in satellite observation, space perception, and spacecraft control. These advancements not only augment the precision and effectiveness of remote sensing but also possess the potential to assist or even supplant humans in executing high-level mission operations autonomously. In the future, AI technologies in space remote sensing are anticipated to assume an increasingly critical role in the exploration, utilization, and development of space resources.

The primary aim of this Special Issue is to consolidate recent advancements and foster interdisciplinary collaboration among researchers and practitioners in the field. It aligns with the journal’s scope by promoting high-impact research that pushes the boundaries of aerospace science and technology. The Special Issue seeks to illuminate the transformative potential of artificial intelligence and its applications in aerospace, thereby contributing to the journal’s mission of disseminating pioneering research and practical insights.

By bringing together diverse perspectives and expertise, this Special Issue aims to provide a comprehensive overview of the current state of intelligent aerospace research and chart a course for future innovations. Submissions are encouraged to explore a variety of themes, including but not limited to, the following topics:

• Application of machine vision in aerospace, especially in remote sensing.
• Intelligent control of spacecraft and space robots.
• Intelligent optimization methods for spacecraft trajectory.
• Space object detection from optical images.
• Machine learning for satellite behavior characterization.
• Intelligent perception and control in planetary exploration.

Prof. Dr. Zhaokui Wang
Dr. Yang Yang
Dr. Lin Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.