Special Issue "Advancement of Remote Sensing in Landslide Susceptibility Assessment"
Deadline for manuscript submissions: 31 October 2023 | Viewed by 7902
Interests: potential landslide detection; landslide susceptibility mapping; deep learning
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Landslides are one of the most widespread natural disasters that pose severe threats to many areas of the world. Landslide susceptibility assessment has proven helpful in designing landslide mitigation strategies for reducing disaster risk and societal and economic losses. Moreover, landslide susceptibility maps are essential for land use planning, hazard prevention, and risk management. In the last decade, remote sensing technology has achieved rapid development, and more and more high spatial and temporal resolution remote sensing images are available to the public for free. It brings an opportunity for the development of landslide mapping and susceptibility assessment techniques, and it becomes possible to obtain higher spatial and temporal resolution landslide susceptibility results.
This Special Issue is dedicated to sharing the latest advances in remote sensing technology for landslide susceptibility assessment. Contributions focused on improving landslide susceptibility assessment methods (e.g., using machine learning or deep learning approaches) are also welcome, but a detailed description of the innovation of the proposed method is needed. We especially encourage the use of freely available remote sensing data and open-source processing software, as it helps us to conduct analysis anywhere in the world. The data and code are recommended to be uploaded as supplementary material. Review papers will also be considered.
Potential topics for this Special Issue may include, but are not limited to, the following:
- Rapid landslide susceptibility mapping using remote sensing data/approaches;
- Update of existing landslide susceptibility maps using remote sensing data/approaches;
- Dynamic analysis of landslide susceptibility using remote sensing data/approaches;
- Application of remote sensing in physical- and statistical-based landslide susceptibility models;
- Application of remote sensing in association with artificial intelligence technology in landslide susceptibility assessment.
Dr. Zhijie Zhang
Dr. Yaning Yi
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.
- landslide susceptibility
- slope instability
- susceptibility modeling
- earth observation
- machine learning
- artificial intelligence