Improving Disaster Damage and Loss Assessments by Modeling and Remote Sensing Techniques

Dear Colleagues,

In recent years, remote sensing (RS) technologies have been used extensively in scientific and engineering applications, together with physics-based and advanced machine learning modeling. Particularly, disaster science research has seen considerable attention, yielding novel methodologies for rapid post-disaster damage assessments and an accurate understanding of hazard scenarios before disasters. On the one hand, integrating numerical modeling and remote sensing technologies grant powerful means to analyze several characteristics of the Earth’s surface ground, such as deformations, growing urban environments, and local site characterization. Furthermore, optical imaging and synthetic aperture radar (SAR) provided complementary information for pre- and post-disaster hazard assessments. However, complex and unique disasters induced by earthquakes, heavy rain, and other natural phenomena present great challenges for RS and modeling technologies because of several factors such as data accessibility, missing information, and high computation costs. This Special Issue explores the theory and application of numerical modeling with RS technologies for disaster damage and loss assessments.
This Special Issue is open to all contributions on recent advances and novel developments of methodologies and best-case study applications in the RS and numerical and machine learing modeling of earthquakes, tsunamis, volcanic, and flooding events. We encourage submissions of both review and original research articles related, but not limited to, the following topics:

• Analysis of urban environment changes;
• RS for urban vulnerability analysis;
• Damage recognition following major disasters;
• Machine learning for disaster research;
• Detection and classification of building damage;
• Extraction of flooded areas from RS data;
• Time series data for surface deformation monitoring;
• Open data and big data for multi-hazard analysis;
• Natural hazard modelling and prediction.

Dr. Bruno Adriano
Dr. Sadra Karimzadeh
Dr. Luis Moya
Dr. Bahareh Kalantar
Dr. Alok Bhardwaj
Dr. Yanbing Bai
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.