Intelligent Perception of Geo-Hazards from Earth Observations

Dear Colleagues,

Geo-disasters are one of the most pervasive natural hazards, which usually result in enormous human casualties and property damage. Detecting and monitoring geohazards over extensive areas with Earth observation technology by using automated methods is an urgent need, yet a challenging task in the practice of disaster prevention and mitigation at present. In recent decades, Earth observation technology in association with deep learning in artificial intelligence (AI) has drawn more and more attention, and great progress has been made particularly on new methods based on convolutional neural networks (CNNs) for geo-disaster detection, risk assessment, monitoring and early warning from optical remote sensing, InSAR, LiDAR and so on. The rapid advancement in this active field has shed light on effective and on-time responses to potential geo-disaster preventions and mitigations over disaster-prone regions.

For fostering the application of advanced machine learning and deep learning algorithms in association with Earth observations for geo-disaster prevention and mitigation, this Special Issue aims to publish works that present the use of any non-invasive technique (satellite and aerial RS, UAV, sensors installed on various equipment) in association with deep learning approaches for geo-disaster detection, susceptibility assessment and mapping, as well as early warning or long-term monitoring over extensive areas. Works devoted to the broadly understood detecting and mapping of potential geo-disasters to their retention properties are also welcomed.

Articles may address, but are not limited, to the following topics:

• Rapid mapping of co-seismic landslides using earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
• Geo-disaster detection and susceptibility mapping by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
• Potential geo-disaster detection and early warning by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
• Long-term geo-disaster monitoring by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches.

Prof. Dr. Qiang Xu
Prof. Dr. Shunping Ji
Prof. Dr. Wanchang Zhang
Guest Editors

Manuscript Submission Information

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