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UID:19@ccr.nelson.wisc.edu
DTSTART;TZID=America/Chicago:20250304T130000
DTEND;TZID=America/Chicago:20250304T140000
DTSTAMP:20250124T201718Z
URL:https://ccr.nelson.wisc.edu/events/empowering-rapid-disaster-mapping-a
 nd-damage-assessment-with-big-sensor-data-and-geoai-case-studies-of-wildfi
 res-and-floods/
SUMMARY:Empowering Rapid Disaster Mapping and Damage Assessment with Big Se
 nsor Data and GeoAI: Case Studies of Wildfires and Floods
DESCRIPTION:Speaker:  Qunying Huang\, Professor of Geography\, UW–Madiso
 n\n\nThe proliferation of real-time\, voluminous data streams generated by
  diverse physical and social sensors\, including satellites\, unmanned aer
 ial vehicles (UAVs)\, ground sensor networks\, and social media\, offers u
 nprecedented opportunities for the comprehensive characterization of disas
 ter scenarios. These data enable the development of innovative approaches 
 to disaster mapping\, damage assessment\, and timely\, data-driven decisio
 n-making in response to natural hazards. Concurrently\, advancements in ar
 tificial intelligence (AI)\, particularly deep learning (DL)\, have driven
  the evolution of geospatial AI (GeoAI) applications with the capacity to 
 quickly and accurately extract valuable insights from extensive geospatial
  datasets\, approximating human-like cognition. The convergence of big sen
 sor data and GeoAI holds transformative potential for enhancing situationa
 l awareness\, operational efficiencies and responsiveness in disaster mana
 gement. In light of these advancements\, this talk explores key challenges
 \, cutting-edge solutions\, and real-world applications that synthesize bi
 g sensor data and GeoAI to extract actionable information for disaster res
 ponse. Using wildfires and floods as case studies\, I will present a suite
  of deep learning based models\, ranging from supervised learning to self-
 learning and weakly supervised learning\, to achieve real-time disaster ma
 pping and damage assessment across diverse events and locations with minim
 al human intervention.\n\nView the livestream
CATEGORIES:CPEP
LOCATION:811 Atmospheric\, Oceanic and Space Sciences\, 1225 W. Dayton Stre
 et\, Madison\, United States
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1225 W. Dayton Street\, Mad
 ison\, United States;X-APPLE-RADIUS=100;X-TITLE=811 Atmospheric\, Oceanic 
 and Space Sciences:geo:0,0
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DTSTART:20241103T010000
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