Geospatial Artificial Intelligence (GeoAI) in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 127
Special Issue Editors
Interests: remote sensing image interpretation; artificial intelligence; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image interpretation; artificial intelligence; machine learning; computer vision
Interests: machine learning; computer vision and natural language processing
Special Issue Information
Dear Colleagues,
With the development of remote sensing technology, hundreds of millions of square kilometers of the Earth can now be covered on a daily basis. Geospatial analysis benefits greatly from the vast amount of remote sensing data. The goal of GeoAI is to enhance the processing power of geospatial information by introducing artificial intelligence technologies. In recent years, an increasing number of researchers have employed GeoAI technology to accomplish the intelligent interpretation of remote sensing data, effectively lowering the bar for geospatial applications. In particular, the emergence of foundation models in the past two years has propelled AI forward with regard to cognitive capabilities, opening up new avenues for geospatial information processing in remote sensing. While some researchers have used remote sensing foundation models and showed initial success in terms of their application in various recognition tasks, there remain some challenging and promising directions for further exploration.
This Special Issue aims at studies on the most advanced GeoAI technology in remote sensing and its diverse application tasks. Topics may cover anything from the development of GeoAI models (especially foundation models) to the efficient generalization applications of the models. Hence, we welcome research that includes issues such as the embedding of remote sensing properties or geospatial knowledge, the robustness and transferability of foundation models, and the future evolution forecasting of geospatial tasks.
In conjunction with the 2024 ISPRS TC I Contest on Intelligent Interpretation for Multi-modal Remote Sensing Application, the Special Issue is open to authors presenting papers at the Contest. It is important to note that papers submitted for this Special Issue should not be identical to the papers presented at the ISPRS TC I Contest. Instead, authors are encouraged to provide longer papers, typically two to three times longer, offering a more comprehensive presentation of their work, enhanced techniques, and methodologies, additional datasets, and expanded experimental sections.
Dr. Xian Sun
Dr. Wanxuan Lu
Dr. Lingqiao Liu
Dr. Shucheng You
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 foundation models with GeoAI
- deep neural networks combining RS properties or geospatial knowledge
- robust architectures for multiple geospatial tasks
- GeoAI for remote sensing forecasting tasks
- high-quality and large-scale datasets for remote sensing foundation models
- applications such as smart cities, land-cover and ocean observation, environmental monitoring, and sustainable development
