Artificial Intelligence for Natural Hazards (AI4NH)

Dear Colleagues,

Earth observation (EO) data show great potential for providing critical information for monitoring and modeling climate-related and geophysical natural hazards. The recent advances in remote sensing relevant technologies have increased the volume and the variety of EO data obtained at various spatial and temporal resolutions. EO-based mapping plays a vital role in disaster preparedness, early warning, emergency management programs, and humanitarian responses by providing timely and accurate information from the Earth's surface.

Moreover, the advent of the era of fast growth in hardware and high-performance computing technologies has resulted in developing and implementing several state-of-the-art deep/machine learning networks for a wide range of tasks with EO data (e.g., monitoring, modeling, and susceptibility mapping natural hazards).

This Special Issue focuses on both supervised and unsupervised deep/machine learning models for converting EO data into valuable information, meaningful patterns for modeling, and the prediction of upcoming cycles of natural hazards. Therefore, we aim to discover and highlight new deep/machine learning models for EO data analysis to gain a better understanding of natural hazards, their environmental effects, risk assessment (and vulnerability), disaster risk reduction, climate adaptation, disaster resilience, and hazard recovery.

This Special Issue invites submissions that may include, but are not limited to, the following natural hazards:

• Landslides
• Submarine landslides
• Volcanoes
• Snow avalanche
• Glaciers
• Earthquakes
• Earthquakes and tsunamis
• Storms
• Land subsidence
• Droughts
• Extreme temperatures
• Floods
• Wildfires/bushfires
• Post-fire debris flow
• Deforestation
• Soil, gully, and piping erosion
• Multi-hazards.

Dr. Omid Ghorbanzadeh
Prof. Dr. Pedram Ghamisi
Guest Editors

Manuscript Submission Information

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