Topic: AI for Space Information and Related Applications
Guest Editor(s)
Prof. Yingkui Gong
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Prof. Wei Yang
School of Electronics and Information Engineering, Beihang University, Beijing, China.
Prof. Zhou Chen
Institute of Space Science and Technology, Nanchang University, Nanchang, Jiangxi, China.
Special Issue Introduction
Recent progress in artificial intelligence (AI) brings more potential opportunities and probabilities for data mining and problem-solving in science and engineering for space and air fields. In particular, the advances in deep learning techniques further accelerate the construction of large or foundation AI models to better approximate the complex systems in geoscience, air traffic and space physics.
This Special Issue focuses on the hot topic of space and air information processing with AI. Our scope not only encompasses scientific topics and issues in space- and air-based remote sensing and communications but also extends to space physics observations and data processing. We encourage the application of popular methods and frameworks in machine learning and deep learning to tackle challenging problems in space information and related areas. We aim to explore the potential applications of foundation models and multimodal frameworks in spaceborne and air-based remote sensing data and space and ground-based observations for space weather analysis. Additionally, we will investigate the potential role of AI in Global Navigation Satellite Systems (GNSS), Synthetic Aperture Radar (SAR) and other space- and air-based techniques.
This Special Issue focuses on the hot topic of space and air information processing with AI. Our scope not only encompasses scientific topics and issues in space- and air-based remote sensing and communications but also extends to space physics observations and data processing. We encourage the application of popular methods and frameworks in machine learning and deep learning to tackle challenging problems in space information and related areas. We aim to explore the potential applications of foundation models and multimodal frameworks in spaceborne and air-based remote sensing data and space and ground-based observations for space weather analysis. Additionally, we will investigate the potential role of AI in Global Navigation Satellite Systems (GNSS), Synthetic Aperture Radar (SAR) and other space- and air-based techniques.
Keywords
Space information, artificial intelligence, remote sensing, deep learning, foundation model
Submission Deadline
30 Jun 2025
Submission Information
For Author Instructions, please refer to https://www.oaepublish.com/ir/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=ir&IssueId=IR241025
Submission Deadline: 30 Jun 2025
Contacts: Amber Ren, Assistant Editor, editorial@intellrobot.com
