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Fusing Multi-sourced Sensing Data and GeoAI for Disaster Management

Authors: Qunying Huang*, University of Wisconsin - Madison
Topics: Cyberinfrastructure, Hazards, Risks, and Disasters, Geographic Information Science and Systems
Keywords: GeoAI, natural hazards
Session Type: Paper
Day: 4/8/2020
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual Track 9
Presentation File: No File Uploaded


Real-time big data streams from both social (e.g., social media) and physical (e.g., remote sensing) sensor networks offer exciting opportunities for characterizing disaster situations, developing novel approaches to assess damage, and making effective decisions during natural hazards. In addition, recent advancements in artificial intelligence (AI) techniques especially the deep learning (DL) methods, have enabled a surge of geospatial AI (GeoAI) applications that are now providing fast and near human-level perception from massive datasets to inform decision makers. This talk explores opportunities, challenges, solutions and applications that synthesize complex sensor data and GeoAI to extract critical, actionable information for various disaster management activities.

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