Deep Learning Meets Remote Sensing for Earth Observation and Monitoring (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 20 December 2024 | Viewed by 184
Special Issue Editors
Interests: computer vision; remote sensing; deep learning; embedded system design
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: image and video analysis; remote sensing; deep learning; pattern recognition; medical imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
After the resounding success of our first edition, we are happy to announce the second edition of our Special Issue.
Remote sensing technologies have enabled researchers to understand, analyze and monitor different activities on Earth from afar. With the current technological advances, such as satellites, drones, etc., significant amounts of data (in the form of high-resolution images) can be easily acquired. This opens up new paradigms and research directions for the remote sensing community that offer different applications in diverse fields, for example, smart agriculture, traffic monitoring, disaster management, and urban planning. For Earth’s monitoring, visual pattern recognition is a pre-processing step. Automated recognition of different patterns by employing computer vision and deep learning techniques will provide crucial information for monitoring changes across the Earth’s surface. Deep learning techniques have achieved tremendous success in object classification, detection, and segmentation tasks in natural images; however, these models face challenges in identifying patterns in remote sensing images due to complex backgrounds, arbitrary views, and large variations in object sizes.
This Special Issue invites authors to submit their original articles regarding the design and development of novel deep learning models to identify different visual patterns to support Earth monitoring. In addition, we would like to invite the submission of research related to remote sensing-based disaster assessment and management support systems. Lastly, we welcome comprehensive review articles that focus on analyzing the performances of state-of-the-art deep learning models in remote sensing imagery.
Submissions may cover a wide range of topics, including the following:
- Deep learning models for the monitoring of Earth;
- Deep learning models for the monitoring of crops using remote sensing data;
- Flood segmentation and natural hazards prediction;
- Road and building footprint extraction for urban growth and planning;
- Remote sensing for smart farming for sustainable agriculture.
Dr. Sultan Daud Khan
Dr. Habib Ullah
Dr. Mohib Ullah
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
- satellite image processing
- multi-scale feature extraction
- deep learning
- context understanding
- data fusion
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