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Intelligent Damage Assessment Systems Using Remote Sensing Data

Special Issue Information

Dear Colleagues,

Earthquakes can be considered one of the most serious natural disasters being faced by countries around the world. They can occur with slight or no notice, and the frequency increases as time goes by. Earthquakes cause massive destruction to the environment, infrastructures, and buildings within affected areas. Human life is also at risk during high-intensity earthquakes, especially in densely populated areas. Since earthquakes are not preventable, innovative pre-emptive technologies should be researched and developed in order to predict occurrences. This does not only have the potential of facilitating evacuation or safety measures, but can also help to improve disaster response actions. Efficient and timely response action allows affected areas to receive assistance faster, especially for distribution of relief resources (i.e., food, medicine and shelter). Recently, remote sensing (RS) technologies have been researched to facilitate disaster response actions. Tools have been developed based on RS to perform damage detection and emergency response systems (i.e., post-earthquake). Other efforts include studies estimating post-earthquake building damage where estimation accuracy varied depending on the type of data being used. Data types also vary from optical sensors, LiDAR point clouds to synthetic aperture radars (SAR) and aerial and unmanned aerial vehicle (UAV) imageries. In recent years, intelligent methods using machine and deep learning methods have become popular for post-earthquake RS-based analysis. Due to the potential in these technologies, this Special Issue invites scholars to share their recently-developed innovations and advances for post-earthquake building damage assessment using remote sensing data and computer vision, contributing to improve disaster resilience.

Dr. Bahareh Kalantar
Dr. Alfian Abdul Halin
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

  • Building
  • Damage
  • Remote sensing
  • Earthquake
  • Machine learning
  • Artificial intelligence
  • Computer vision

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Remote Sens. - ISSN 2072-4292Creative Common CC BY license