Intelligent Interpretation of Remote Sensing Imagery: AI-Enhanced Monitoring of Geographical and Geological Environments
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 41
Special Issue Editors
Interests: geological and geographical remote sensing
Interests: geoscience big data analysis; deep learning
Special Issue Information
Dear Colleagues,
The intelligent interpretation of remote sensing imagery has undergone a revolutionary transformation, driven by rapid advances in Artificial Intelligence (AI). AI-enhanced techniques, particularly deep learning, have dramatically improved our capacity to automatically and accurately decipher complex information from aerial and satellite images. This capability is pivotal for monitoring and understanding the dynamic processes of geographical and geological environments, which are fundamental to resource management, environmental protection, and geohazard mitigation.
This Special Issue is dedicated to showcasing the latest breakthroughs in AI-driven methodologies for the interpretation of remote sensing data, with a specific focus on applications within geographical and geological systems. By harnessing the power of AI, we aim to move beyond traditional analysis towards the development of robust, efficient, and intelligent monitoring frameworks. These advancements are crucial for building resilient infrastructure, ensuring sustainable land use, and improving our understanding of Earth's surface processes.
We invite the submission of original research and review articles that explore innovative algorithms, models, and applications. Topics of interest include, but are not limited to, the following:
- AI Models for Remote Sensing: Novel deep learning architectures for image classification, segmentation, and object detection.
- Geological and Geohazard Monitoring.
- Geographical Environment Analysis.
- Engineering and Infrastructure Applications.
- Multi-source Data Fusion: Integration of optical, SAR, LiDAR, and hyperspectral data for a comprehensive environmental understanding.
- Change Detection and Time-Series Analysis: Monitoring dynamic changes in geographical and geological landscapes over time.
We welcome contributions that address the challenges and opportunities in intelligent remote sensing interpretation, with manuscripts focusing on novel algorithms, benchmark datasets, and compelling case studies that demonstrate the transformative potential of AI in geographical and geological sciences.
Dr. Yusen Dong
Dr. Yuewei Wang
Dr. Kang He
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
- remote sensing
- deep learning
- multi-sensor data fusion
- image segmentation
- change detection
- explainable AI
- geographical environment
- geological environment
- hazard monitoring
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