1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advan...1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advances in satellite-based emergency mapping,delays persist highlighting the need to enhance early warning systems and overcome limitations of conventional,observation-based workflows.The integration of Big Data and Artificial Intelligence(BD&AI)through the fusion of available vast Remote Sensing(RS)datasets with Machine Learning(ML)algorithms allows us to achieve near real-time monitoring of hazards,improve their prediction.展开更多
文摘1.Introduction When natural hazards such as floods,droughts,and wildfires continue to escalate globally,timely acquisition of disaster information remains crucial for rapid on ground situation assessment.Despite advances in satellite-based emergency mapping,delays persist highlighting the need to enhance early warning systems and overcome limitations of conventional,observation-based workflows.The integration of Big Data and Artificial Intelligence(BD&AI)through the fusion of available vast Remote Sensing(RS)datasets with Machine Learning(ML)algorithms allows us to achieve near real-time monitoring of hazards,improve their prediction.
