GeoAI and Deep Learning Symposium: Big data and GeoAI for natural hazards
Recently, we have unfortunately witnessed a series of deadly hurricane events (e.g., Harvey, and Florence) 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.
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
Qunying Huang, qhuang46@wisc.edu;
Zhenlong Li, zhenlong@sc.edu;
Xinyue Ye, xinyue.ye@njit.edu
