Deep Learning Innovations in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 264
Special Issue Editors
Interests: GIScience; remote sensing; deep learning
Interests: geographical information science; spatial analysis and modeling; remote sensing; climate change; land cover land use change
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; GIScience; environmental science; data science; geography
Interests: semantic and knowledge graph; data interoperability and provenance; exploratory data analytics and visualization; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Interests: spatial modeling; big data; machine learning; and data mining
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; geospatial analysis; climate change; hydrology; land use and land cover; health geography; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancements in deep learning techniques have revolutionized various fields, and remote sensing is no exception. This Special Issue aims to highlight the latest developments, applications, and challenges of deep learning in the realm of remote sensing. Advanced models, including Generative Adversarial Networks (GANs), Large Language Models (LLMs), and diffusion models, have demonstrated significant promise in extracting meaningful information from vast and complex remote sensing data. This has implications for numerous applications, including land cover classification, object detection, change detection, and anomaly detection.
This collection of articles seeks to bring together contributions from researchers around the globe to discuss innovations in network architectures, training methodologies, and data preprocessing techniques. We particularly encourage submissions that investigate applications in environmental, urban, or climate studies. Such investigations could delve into monitoring deforestation, assessing urban sprawl, predicting climate change impacts, and more. Furthermore, the issue will explore the integration of deep learning models with traditional methods, enhancing the accuracy and efficiency of remote sensing analyses. Challenges associated with data quality, computational costs, and model interpretability will also be addressed. By presenting state-of-the-art research and practical case studies, this Special Issue aims to serve as a valuable resource for scientists, engineers, and practitioners “dedicated to advancing the field of remote sensing through the power of deep learning.
Dr. Zhe Wang
Dr. Chao Fan
Dr. Sanaz Salati
Dr. Marshall (Xiaogang) Ma
Dr. Xiang Que
Dr. Hui wang
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
- GeoAI
- deep learning
- machine learning
- GAN, LLM, SAM, and generative AI
- image processing and pattern recognition
- artificial intelligence
- GIS
- geostatistics
- spatial modeling
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
