Special Issue "Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (20 January 2019).
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
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Natural hazards are extreme and unexpected threats resulting from natural processes of the Earth, such as landslides, floods, hurricanes, tornados, volcanoes, or any other natural phenomena that may cause harm to humans.
In this sense, this Special Issue encourages authors to share recent advances in natural hazard management, with a particular emphasis on issues addressed by means of advanced machine learning and big data analytics and remote sensing techniques.
Massive amounts of data are stored in almost all disciplines. Remote sensing is not an exception, since very large time series or high-resolution satellite and aerial images are sources of valuable information. How to extract useful information from these big data sources is not, by contrast, an easy task due to the computational and infrastructural costs involved.
Very powerful approaches have been developed in the context of advanced machine learning and big data analytics during the last few years. Such approaches deal with large datasets, considering all samples and measurements, as well as including many additional features. With them, advanced machine learning and big data methods for extracting relevant patterns, high performance computing or data visualization are being nowadays successfully applied to the field of remote sensing.
For all the aforementioned, we kindly invite the scientific community to contribute to this Special Issue by submitting novel and original research addressing at least one of the following topics, in the context of data science and big data:
- Recent advances in information fusion for natural hazards management.
- Recent advances in spatial modeling for natural hazards management.
- Recent advances in temporal modeling for natural hazards management.
- Real-world case study with findings with clear interest to the scientific community.
Finally, authors are encouraged to share codes and data so that their studies can be easily reproducible and serve as seed for future improvements.Prof. Dr. Dieu Tien Bui
Prof. Dr. Francisco Martínez-Álvarez
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 papers will be 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 2200 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.
- Data science and big data analytics
- Remote sensing
- Spatio-temporal analysis
- Information fusion
- Natural hazards management