Advancements in Remote Sensing and Artificial Intelligence for Geohazards
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 367
Special Issue Editors
Interests: landslide; floods; artificial intelligence; remote sensing
Interests: artificial intelligence; natural hazards; remote sensing; GIS
Interests: landslides; floods; sinkholes; remote sensing; risk analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geohazards frequently cause significant human and economic losses worldwide, presenting persistent challenges to social and urban sustainability. It is of great urgency to accurately, timely, and effectively evaluate, prevent, and control geohazards and to clearly understand their evolutionary characteristics, trends, and rules. With the rapid development of remote sensing (RS), along with the success of artificial intelligence (AI), they have increasingly become vital technologies for geohazard perception, cognition, prediction, and so on. Correspondingly, we have also witnessed more effective prevention and controlling of various types of geohazards, as well as significant decreases in social and economic losses from such phenomena. It is necessary to deeply explore and greatly facilitate the application of remote sensing in AI in geohazards so that we may overcome the difficulties caused by geohazards.
This Special Issue aims to gather studies covering the applications of and advancements in RS and AI for all types of geohazards. Multisource RS data, state-of-the-art AI algorithms, and manifold geohazard prevention services, among other issues, are welcome.
Article types include Articles, Reviews, Case Reports, Data Descriptors, and Technical Notes. Articles may address, but are not limited to, the following topics:
- Geohazard monitoring and detection;
- Geohazard risk assessment and vulnerability;
- Geohazard prediction and spatial modeling;
- Geohazard evaluation;
- Geohazard emergency rescue;
- Geohazard simulation;
- Geohazard evolution;
- Disaster chain;
- Multiple hazards;
- Geohazard datasets.
Prof. Dr. Xianmin Wang
Dr. Omid Ghorbanzadeh
Prof. Domenico Calcaterra
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
- artificial intelligence
- geohazards
- deep/machine learning
- evolutionary computation
- slope failure
- geohazard chain
- floods
- dam breach
- surface collapse
