Remote Sensing and Geospatial Technologies for Earthquake Disaster Assessment and Management
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".
Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 342
Special Issue Editors
Interests: earthquake forecasting; seismology; seismic data analysis; seismic imaging
Interests: satellite remote sensing; robust satellite techniques for natural; environmental and industrial risks forecast and monitoring: floods, forest fires, earthquakes, volcanic eruptions, sand storms, air and water pollution, oil spills and energetic pipelines accidents
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We warmly invite you to contribute manuscripts to a Special Issue of “Remote Sensing and Geospatial Technologies for Earthquake Disaster Assessment and Management” in Remote Sensing.
With the fast development of sensing technology, computing capability, and artificial intelligence (AI), remote sensing (RS) technologies are more widely used for earthquake studies, including long-term risk assessment, forecasting, early warning, nucleation mechanism, fluid-injection-induced seismicity, and disaster management. Due to the various sources and formats of RS datasets, earthquake forecasting, and its related hazard assessment, mitigation and management are becoming unprecedentedly efficient and reliable.
In this Special Issue, we welcome submissions that showcase the recent advances in the integration of RS technologies and earthquake studies, including but not limited to advanced geospatial sensing, processing, and imaging technologies for enhanced earthquake forecasting and hazard mitigation, cutting-edge earthquake forecasting techniques that better leverage the traditional RS datasets, fundamental understanding of earthquake nucleation mechanism, breakthroughs in AI-assisted geospatial data analysis that make earthquakes better predicted or understood, and open-access datasets and software for algorithm benchmarking.
We appreciate your consideration in submitting manuscripts to this Special Issue on RS and earthquake studies. We also kindly request your assistance in sharing this announcement with esteemed colleagues, encouraging them to contribute their expertise to this important field of study.
Prof. Dr. Yangkang Chen
Prof. Dr. Valerio Tramutoli
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
- earthquake forecasting
- earthquake early warning
- geospatial data analysis
- remote sensing
- hazard assessment
- disaster management