Geospatial Artificial Intelligence for Natural Hazards
Type: Virtual Paper
Day: 3/1/2022
Start Time: 5:20 PM
End Time: 6:40 PM
Theme:
Sponsor Group(s):
Geographic Information Science and Systems Specialty Group
, Remote Sensing Specialty Group
, Cyberinfrastructure Specialty Group
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Organizer(s):
Bo Peng
, Qunying Huang
, Song Gao
, Daniel Wright
Chairs(s):
Bo Peng, Department of Geography, University of Wisconsin-Madison
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Description:
Artificial intelligence (AI) is playing a critical role in mining geospatial data captured via physical sensing (e.g., remotely sensed imagery) and social sensing (e.g., social media). The surge of geospatial AI (GeoAI) techniques have enabled large-scale real-time natural hazard response (e.g., flood mapping, damage assessment, and disaster event detection) by leveraging advanced data-driven deep learning models. However, current GeoAI techniques for natural hazards are still challenged by various issues, including the lack of human annotations for training deep learning models in real time, poor model generalizability, data deficiency, inefficient large-scale data processing and visualization, uninterpretable black-box models, etc.
Presentation(s), if applicable
Bing Zhou, Pennsylvania State University; Analysis of Linguistic and Geographic Disparities Across Hurricane Related Rescue Request Social Media Data |
Joseph Aro, ; Climate Change in Urban Lagos - An existential scenario for the urban poor |
Dimitris Gounaridis, University of Michigan; Using Geospatial Data Science to Identify Vulnerable Communities to Climate Change |
Jirapa Vongkusolkit, ; Physics-Informed Weakly Supervised Learning for Near Real-Time Flood Mapping |
Myung Sik Cho, Michigan State University; Quantification of spatial effects of dams on watersheds using watershed topology and cloud computing platform |
Non-Presenting Participants Agenda
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Geospatial Artificial Intelligence for Natural Hazards
Description
Virtual Paper
Contact the Primary Organizer
Bo Peng - bo.peng@wisc.edu
Session Sponsored By
