Emerging Paradigms in Earth Vision: Towards General and Scalable Remote Sensing Intelligence
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 45
Editors
Interests: multi-modal learning; remote sensing interpretation
Interests: remote sensing image super-resolution; multi-modal learning
Special Issues, Collections and Topics in MDPI journals
Interests: low-quality image reconstruction and target recognition; hyperspectral remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of Earth observation technologies, remote sensing big data is continuously accumulating at an unprecedented rate, offering massive multi-modal, multi-resolution, and multi-temporal imagery. This data explosion has laid a solid foundation for the intelligent interpretation of our planet. However, conventional deep learning models in remote sensing largely follow a "one-model-one-task" or "data-hungry" paradigm. They heavily rely on massive, precisely annotated datasets and often suffer from severe performance degradation when facing unseen scenes, varying sensor characteristics, or complex environmental shifts.
To overcome these bottlenecks, the field of "Earth Vision" is currently witnessing a profound paradigm shift. We are moving from task-specific, isolated algorithms toward general, scalable, and robust intelligent systems. Emerging paradigms—such as geospatial foundation models, self-supervised learning, vision-language models, and open-world learning—are redefining how we extract knowledge from remote sensing data. Developing general and scalable remote sensing intelligence is not merely a theoretical pursuit; it is of critical importance for establishing highly automated, global-scale, and next-generation Earth observation systems that can address urgent global challenges like climate change, disaster monitoring, and sustainable development.
This Special Issue aims to provide a premier platform for researchers to present cutting-edge breakthroughs, novel architectures, and theoretical advancements that drive the paradigm shift in remote sensing intelligence. We encourage contributions that explore how to construct more generalizable, adaptable, and scalable AI frameworks capable of handling the complex, heterogeneous nature of remote sensing data.
The subject perfectly aligns with the core scope of Remote Sensing, as it bridges advanced artificial intelligence with Earth observation data processing, ultimately pushing the boundaries of automated remote sensing image analysis and its diverse applications.
Research articles, review articles, and short communications are invited. Topics of interest include, but are not limited to, the following:
- Geospatial Foundation Models and Large Vision-Language Models for Earth Observation;
- Self-Supervised, Weakly Supervised, Semi-Supervised, and Unsupervised Learning in Remote Sensing;
- Multi-Modal and Cross-Sensor Data Fusion for Generalized Interpretation;
- Open-World Learning (e.g., Zero/Few-Shot learning, Open-Vocabulary Recognition);
- Domain Generalization and Cross-Domain Adaptation in Remote Sensing;
- Efficient and Scalable AI Architectures (e.g., Lightweight Models, Edge Computing);
- Physics-Informed Machine learning for robust remote sensing analysis;
- Prompt engineering and fine-tuning strategies for downstream tasks;
- Spatio-temporal dynamic reasoning and continuous learning.
Dr. Hailong Ning
Dr. Sen Lei
Dr. Le Dong
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-anonymized 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.
Keywords
- earth vision
- geospatial foundation models
- general artificial intelligence
- scalable deep learning
- open-world learning
- multi-modal fusion
- self-supervised learning
- domain generalization
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