Intelligent Damage Assessment Systems Using Remote Sensing Data

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

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