Artificial Intelligence and Deep Learning Symposium: Big Data and Mining for Natural Hazards

Type: Paper
Theme: Hazards, Geography, and GIScience
Sponsor Groups: Cyberinfrastructure Specialty Group
Poster #:
Day: 4/13/2018
Start / End Time: 5:20 PM / 7:00 PM (Eastern Standard Time)
Room: Napoleon D1, Sheraton 3rd Floor
Organizers: Qunying Huang, Zhenlong LI, Xinyue Ye
Chairs: Zhenlong LI


Recently, we have unfortunately witnessed a series of deadly hurricane events (e.g., Harvey, Irma, Jose, Maria and Nate) and Northern California wildfires. Such events claim many lives, cause billions of dollars of damage to properties, and severely impact the environment. When a natural hazard occurs, managers and responders need timely and accurate information on damages and resources to make effective response decisions and improve management strategies. This information is referred to as “Situational Awareness” (SA), i.e., an individually as well as socially cognitive state of understanding “the big picture” during critical situations. Fortunately, the popularity and advancement of social and physical sensor networks offer various real-time big data streams for establishing SA. For example, sharing information such as texts, images, and videos through social media platforms enables all citizens to become part of a large sensor network and a homegrown disaster response team. The use of Unmanned Aerial Vehicle (UAVs) images is offering increasing opportunities in disaster-related situations. However, such massive and rapidly changing data streams present new grand challenges to mine actionable data and extract critical validated information for various disaster management activities.
As part of the AAG 2018 Symposium on Artificial Intelligence and Deep Learning in Geospatial Research, this session explores and captures the innovative machine learning and data mining algorithms, techniques and approaches to generate various useful information and products (e.g., hazard maps), which in turn can assist in disaster management during a natural hazard. Topics of particular interest are, but are not restricted to:
1. Mining and extracting actionable information for rapid emergency response and relief coordination
2. Integrating data mining (machines) and crowdsourcing (human) to support decision-making
3. Topic modeling and event detection
4. Physical infrastructure (e.g., roads, bridges and buildings) feature extraction with deep learning
5. Damage assessment using very high-resolution images or/and social media data
6. Coding/classifying text messages during a natural hazard
7. Identifying or/and matching the needs of people in impacted communities
8. Spatiotemporal mining of social media data to gain geographic situational awareness during a disaster
9. Mining, mapping and visualizing public’s behaviors, opinions or sentiments towards a disaster event
10. Innovative approaches to synthesizing and mining multi-sourced social and physical sensing data for disaster management

To present a paper in the session, you will first need to register and submit your abstract online (, and then email your presenter identification number (PIN), paper title, and abstract to,, or by Oct 25, 2017. High quality research presentations will be invited to submit a full paper to the special issue on “Social Sensing and Big Data Computing for Disaster Management” in the International Journal of Digital Earth (IJDE) (

Qunying Huang,
Department of Geography
University of Wisconsin-Madison, Madison

Zhenlong Li,
Department of Geography,
University of South Carolina

Xinyue Ye,
Department of Geography,
Kent State University


Type Details Minutes Start Time
Presenter Qunying Huang*, University of Wisconsin - Madison, Christopher Scheele, UW-Madison, Spatial Text Mining: An Enhanced Text-Mining Framework for Disaster Relevant Social Media Data Classification 20 5:20 PM
Presenter Jayakrishnan Ajayakumar*, Dept of Geography, Kent State University, Eric Shook, Department of Geography, Environment & Society, University of Minnesota, V. Kelly Turner, Dept of Geography, Kent State University, Extracting Contextual Information from Spatio-Temporal Social Media Data during Extreme Events using Socio-Environmental Data Explorer (SEDE): A Case Study based on Social Media Response to Tornadoes in the United States 20 5:40 PM
Presenter Lei Zou*, Louisiana State University, Nina Lam, Louisiana State University, Heng Cai, Louisiana State University, The Changing Roles of Social Media in Disaster Resilience 20 6:00 PM
Presenter Yuqin Jiang*, University of South Carolina, Zhenlong Li, University of South Carolina, Understanding Evacuation Decision based on Twitter Network 20 6:20 PM

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