Deep Learning-Based Interpretation and Processing of Remote Sensing Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 11
Special Issue Editors
Interests: remote sensing image processing; computer vision; 3D reconstruction; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; machine learning; remote sensing image processing; forest management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid growth of satellite, aerial, and UAV remote sensing technologies has produced massive volumes of multi-source and high-resolution imagery. Effectively interpreting these data is crucial for understanding environmental dynamics, supporting sustainable development, and responding to natural disasters. In recent years, deep learning, together with the emergence of foundation models, large pre-trained architectures, and vision language models, has revolutionized remote sensing image processing by enabling more powerful data-driven feature extraction, semantic understanding, and automated interpretation. These advances are reshaping applications across environmental monitoring, urban analysis, agriculture, forestry, and climate research, while driving a new era of intelligent and scalable geospatial analytics.
This Special Issue aims to showcase innovative research and emerging methodologies in deep learning-based remote sensing image interpretation and processing, including recent developments related to foundation models, pre-training models, vision language model, multi-modal learning, and other advanced AI architectures. We invite contributions that bridge theory and practice, enhance model robustness and interpretability, and advance intelligent systems for geospatial data understanding. The topics align closely with the journal’s scope, emphasizing interdisciplinary research combining remote sensing, computer vision, artificial intelligence, and environmental science.
Suggested themes and article types for submission.
- Image classification, detection, and segmentation;
- Forest fire, smoke, and disaster detection;
- Forestry pest and disease detection;
- Change detection and spatiotemporal analysis;
- Object extraction and scene understanding;
- Hyperspectral, multispectral, SAR, and LiDAR data analysis;
- Foundation models, pre-training models, vision language models, and other advanced AI architectures;
- Self-supervised, transfer, and federated learning methods;
- Benchmark datasets and evaluation protocols.
Dr. Sheng Xu
Prof. Dr. Qiaolin Ye
Dr. Yu Shen
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
- RS image processing and interpretation
- RS image classification, segmentation, and detection
- forest fire, smoke, and disaster detection
- forestry pest and disease detection
- object detection and scene understanding
- hyperspectral/multispectral/SAR/LiDAR data analysis
- foundation models and pre-training models
- advanced AI architectures
- self-supervised, transfer, and federated learning methods
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