Artificial Intelligence for Remote Sensing: State-of-the-Art Reviews and Future Directions

Dear Colleagues,

Artificial Intelligence (AI) has rapidly transformed the field of remote sensing, enabling unprecedented advances in data processing, interpretation, and application. With the increasing availability of multi-source remote sensing data, AI techniques such as machine learning, deep learning, and foundation models are now central to addressing complex Earth observation challenges. These methods not only enhance traditional tasks such as image restoration, classification, detection, and segmentation but also extend to emerging areas including natural language interaction with remote sensing data, multimodal fusion, and automated systems. At the same time, issues of scalability, interpretability, and reliability continue to pose scientific and practical challenges, highlighting the need for systematic reviews and forward-looking perspectives.

This Special Issue is dedicated exclusively to high-quality review papers that provide comprehensive surveys of AI techniques in remote sensing. The objective is to deliver a structured and authoritative overview of the current state of the art, covering algorithmic advances, methodological taxonomies, technological developments, and application-driven innovations. By consolidating knowledge across machine learning, deep learning, and emerging AI foundation models, this Special Issue will highlight both the achievements and limitations of existing approaches. Such reviews are intended to serve as essential references for new researchers seeking to understand the foundations of AI in remote sensing, as well as for experienced scholars aiming to identify open challenges, future research directions, and opportunities for interdisciplinary collaboration.

We welcome submissions of review, survey, and perspective articles on, but not limited to, the following topics:

• AI foundation models in remote sensing;
• Image enhancement and restoration;
• Remote sensing image classification and segmentation;
• Multimodal and temporal data fusion;
• Natural language processing for remote sensing;
• Object/change/anomaly detection;
• Algorithm optimization and acceleration;
• Image stitching and georeferencing;
• Remote sensing image compression;
• AI security in remote sensing.

Prof. Dr. Fa-Ming Fang
Guest Editor

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.