Manuscript Topics

Dear Colleagues,

The fast growth in hardware and high-performance computing technologies has led to introducing, improving, and employing several state-of-the-art deep/machine learning algorithms for a wide range of tasks in very different fields.

Earth Observation (EO) and remote sensing data have considerably advanced due to the incredible progress in relevant technologies. Moreover, online cloud computing platforms allow analytics within big data, allowing large amounts of EO data to be processed faster through advanced deep/machine learning algorithms. It means that the resulting geospatial information can efficiently assist in disaster monitoring and further prevention and mitigation management.

For fostering the application of deep/machine learning algorithms in association with EO data for disaster monitoring, this Special Issue aims to publish works that present the use of such data in association with deep/machine learning algorithms for annotating, modeling, and susceptibility mapping, environmental effects, risk assessment, as well as an early warning or long-term monitoring over local, national or even worldwide areas. Works devoted to the broadly understood monitoring and modeling of potential natural disasters to their retention properties are also welcomed.

This Special Issue invites submissions that may include but is not limited to the following natural disasters:
• Natural hazards
• Earthquakes
• Earthquake and Tsunami
• Storm
• Land subsidence
• Drought
• Extreme temperature
• Floods
• Wildfire/Bushfire
• Post-fire debris flow
• Deforestation
• Soil, Gully, and Piping erosion
• Multi-hazards
• Landslides
• Submarine landslides
• Volcanoes
• Snow avalanche

Keywords    
• Artificial intelligence    
• Machine learning    
• Deep learning    
• Optical data    
• SAR images    
• Time series analysis    
• Spatial modeling    
• Susceptibility mapping    
• Risk assessment

Instruction for Authors
http://www.aimspress.com/aimsgeo/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/