Dr. Xiao Yu: AI Remote Sensing Scientist, focusing on applying satellite and geodetic tools, e.g., SAR & InSAR and GPS, and machine learning (ML) approaches to characterize land deformation associated with hydrologically driven geohazards and land surface processes in various landscapes and geodynamic settings (e.g., landslides, winter storms, and earthquakes), and further investigate the natural or anthropogenic triggers and mechanisms using statistical, analytical, and numerical models.
Abstract
The frequency of extreme hazards due to climate change and human activities has escalated across the world. An accurate and timely response becomes crucial for disaster management and mitigation. Satellite and geodetic approaches have been applied in evaluating the changes of land surface and land motion. The swift progression of AI development greatly assists the monitoring, assessing, modeling, and analyzing process. This presentation will discuss the application of ML/DL techniques in assessing land surface changes, with a focus on evaluating the impacts of various natural hazards. Through case studies involving different geohazards, such as earthquakes, storms, and land subsidence, the presentation will discuss how advanced ML/DL models can analyze satellite imagery, remote sensing data, and geospatial information to detect and quantify surface deformations and land cover changes in different urban area. The presentation will showcase the effectiveness of these models in improving the precision of hazard damage assessments, offering insights into the potential for rapid response and decision-making faced with hazards.
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