New Insights in Remote Sensing Image Interpretation with Deep Learning
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 October 2025 | Viewed by 242
Special Issue Editors
Interests: artificial intelligence; deep learning; remote sensing; semantic segmentation; change detection
Interests: deep learning framework; remote sensing image processing; semantic segmentation; large foundational model; vector extraction
Interests: remote sensing; deep learning; computer vision; 3D modeling
Interests: deep learning; vector data rendering, and processing; GIS applications; artificial intelligent applications in GIS and RS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing image interpretation is essential for monitoring environmental changes, managing natural resources, supporting disaster response, and urban planning. It provides immediate information about the Earth’s surface conditions, helping decision-makers to make scientifically sound judgments. In recent years, advances in remote sensing technology have enabled us to collect vast amounts of data, but interpreting these data remains a significant challenge due to their complexity and variability.
This Special Issue focuses on the latest advances in the field of remote sensing image interpretation, especially the application of deep learning technology. In the past few years, deep learning has greatly promoted the development of remote sensing data processing, significantly improving the performance of tasks such as object detection, semantic segmentation, image classification, change detection, and so on. Given the broad scope of the Remote Sensing journal, which encompasses various aspects of computer vision and pattern recognition, this Special Issue aligns well with the journal’s mission to promote cutting-edge research in these areas.
This Special Issue aims to bring together the latest research results and explore how advanced deep learning models can be used to overcome the challenges faced by traditional methods such as semantic segmentation and change detection for complex scenes, small samples, and multi-modal data fusion. We invite original research papers on the design of novel network architectures, large-scale pre-trained models, cross-domain adaptation, and multimodal data fusion processing algorithms in order to provide new insights into remote sensing image interpretation. We welcome submissions of research articles, review papers, and case studies that contribute to the theoretical and practical advancement of this field. We also welcome the latest large foundational models that apply to remote sensing image semantic segmentation, object detection, change detection, and vector target extraction.
We look forward to your contributions that will further advance the theory and practice of this field.
Dr. Shiyan Pang
Dr. Mi Zhang
Dr. Huiwei Jiang
Dr. Yongyang Xu
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 100 words) can be sent to the Editorial Office for announcement on this website.
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.
Keywords
- artificial intelligence
- deep learning
- remote sensing
- semantic segmentation
- change detection
- classification
- object detection
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