About this Research Topic
Climate-related and geophysical natural hazards can be monitored and modeled using Remote Sensing (RS) data. Due to recent advances in Earth Observation technologies, RS data at different spatial and temporal resolutions are available in greater quantities than ever before. By providing timely and accurate information from the Earth's surface, earth observation-based mapping plays an essential role in disaster preparedness, early warning programs, emergency management programs, and humanitarian efforts.
Furthermore, with the advancement of hardware and high-performance computing technologies, several deep and machine learning networks have been designed and implemented for a wide range of tasks in RS data (e.g., monitoring, modeling, and susceptibility mapping natural hazards).
This Research Topic investigates deep/machine learning networks for transforming RS data into useful information, exploring meaningful patterns for modeling, and predicting the upcoming cycles of natural hazards. Consequently, our goal is to discover and describe new deep and machine learning networks for RS data analysis to gain a better understanding of natural hazards, the effects they have on the environment, assessment of hazards (and vulnerability), disaster risk reduction, climate adaptation, disaster resilience, and hazard recovery.
This Research Topic calls for submissions that may include but is not limited to the following natural hazards:
• Landslides;
• Submarine landslides;
• Volcanoes;
• Snow avalanche;
• Glaciers;
• 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.
Furthermore, with the advancement of hardware and high-performance computing technologies, several deep and machine learning networks have been designed and implemented for a wide range of tasks in RS data (e.g., monitoring, modeling, and susceptibility mapping natural hazards).
This Research Topic investigates deep/machine learning networks for transforming RS data into useful information, exploring meaningful patterns for modeling, and predicting the upcoming cycles of natural hazards. Consequently, our goal is to discover and describe new deep and machine learning networks for RS data analysis to gain a better understanding of natural hazards, the effects they have on the environment, assessment of hazards (and vulnerability), disaster risk reduction, climate adaptation, disaster resilience, and hazard recovery.
This Research Topic calls for submissions that may include but is not limited to the following natural hazards:
• Landslides;
• Submarine landslides;
• Volcanoes;
• Snow avalanche;
• Glaciers;
• 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.
Keywords: Artificial intelligence, Machine learning, Deep learning, Optical data, SAR images, Time series analysis, Spatial modeling, Susceptibility mapping, Risk assessment, Hazard detection, Natural hazards, Semantic segmentation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
