Dear colleagues,
We are thrilled to announce the upcoming American Geophysical Union (AGU) Fall Meeting scheduled for December 11-15, 2023, in San Francisco, with online participation options as well. Our session this year is "Empowering Disaster Resilience with Big Data, GeoAI, and Digital Twins (NH015)" in the Natural Hazards Section. We're seeking innovative contributions that use advanced GIScience technologies to model and manage disaster risks and resilience!
Share your work on using geospatial big data for disaster assessment, techniques in enhancing resilience, or GeoAI-assisted analysis. Let's collaborate to build stronger, more resilient communities.
Abstract submissions close on August 2.
Detailed information about the symposium can be found on this webpage: https://www.geoearlab.com/post/call-for-abstracts-empower-disaster-resilience-at-the-agu-fall-meetin...
Please direct any queries to us and we look forward to your participation!
CALL FOR PAPERS
Session: Empowering Disaster Resilience with Big Data, GeoAI, and Digital Twins
Due to climate change, the intensity and frequency of natural disasters, such as hurricanes, earthquakes, wildfires, etc., have increased dramatically over the past decades, and the trends are projected to continue. Many of these events occur in densely populated areas, resulting in significant damage to human society. Other types of disasters, such as health crises, can also have adverse effects on communities. There is an urgent need to improve community resilience and achieve sustainability, which has drawn significant attention from the governments, researchers, and the public. With the ultimate goal of building communities with low disaster risk and vulnerability, the concept of ‘resilient’ has evolved from ‘strong and static’ to ‘flexible and resilient’. Climate change, globalization, risk prediction, and warning systems all contributed to the proliferation of such frameworks.
In recent years, technological advancements in digital resources have revolutionized the gathering and production of geospatial data. Geospatial big data collected fromlike satellite images, social media, street view photos, smartphone applications, surveillance vehicles, and crowdsourcing tools have provided a unique lens to observe real-time human behaviors, environmental changes, and urban dynamics during disaster cycles from multiple dimensions at an unprecedented level. Concurrently, advances in GIScience methods, including GeoAI and digital twins, are becoming increasingly popular in modeling, simulating, and visualizing disaster vulnerability, risk, and resilience, as well as to develop effective mitigation strategies.Our session invites manuscripts with novel concepts in disaster risk, vulnerability, and resilience modeling from various disciplines leveraging the new advances of GIScience. Potential topics may include but are not limited to:
- Novel frameworks for assessing and modeling individual and community vulnerability, risk, and resilience to multiple types of disasters.
- Emerging geospatial big data for disaster vulnerability, risk, and resilience assessment, prediction, and management, e.g., drone footage, nighttime light images, social media, street view images, mobility data, etc.
- Social, geographical, and environmental injustice in disaster vulnerability, risk and resilience.
- Advanced techniques in modeling and enhancing disaster resilience, e.g., urban digital twin systems, virtual reality, augmented reality, etc.
- Development of cyberinfrastructure, tools, and platforms for disaster risk, vulnerability, and resilience modeling and visualization
- Applications to empower different phases of disaster management, i.e., preparedness, response, recovery, and mitigation, using big data analytics and strategies, deep learning approaches, and responsible AI.
- Insights into disaster impacts, e.g., food and water insecurity, infrastructure malfunctions, fatalities and injuries, economic impacts, etc., using big data sources and cloud-based architecture
- AI aided (e.g., deep learning, computer vision, natural language processing) remote sensing and social media data mining in disaster management and resilience assessment.
- Time series modeling and prediction for disaster management and resilience assessment.
- Multimodal data fusion (e.g., social media text, images, remote sensing images, street view images) in disaster management and resilience assessment.
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Debayan Mandal,
Ph.D. Graduate Student,
Geospatial Exploration and Resolution (GEAR) Lab,
Texas A&M University
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