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Collaboration Work: Handbook of Geospatial Artificial Intelligence

GEAR Lab members recently published a chapter providing a comprehensive understanding about GeoAI for Disaster Response (Chapter 14|18 pages) in the Handbook of GeoAI edited by by Dr. Song Gao (the University of Wisconsin – Madison), Dr. Yingjie Hu (University at Buffalo), Dr. Wenwen Li (Arizona State University).

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography.The details of this handbook can be found below:

The chapter led by Dr. Lei Zou, GeoAI for Disaster Response, focuses on the use of GeoAI and Big Data in supporting disaster response and rescue missions. Disaster response plays a vital role in reducing disaster impacts and building resilient communities. Efficient disaster response relies heavily on timely information describing disaster impacts and local needs to coordinate first responders and allocate resources. Geospatial big data offer a novel channel to observe time-sensitive, disaster-related information that can support effective disaster response. However, accurately identifying valuable information from geospatial big data and applying it in disaster response is technically and practically challenging. The emergence of Geospatial Artificial Intelligence (GeoAI) provides new opportunities. This chapter aims to foster the convergence of GeoAI and disaster response with three objectives: (1) establishing a comprehensive paradigm that expounds upon the diverse applications of GeoAI with geospatial big data towards enhancing disaster response efforts; (2) exhibiting the employment of GeoAI in disaster response through the analysis of social media data during the 2017 Hurricane Harvey with advanced Natural Language Processing models; and (3) identifying the challenges and opportunities associated with the complete realization of GeoAI's potential in disaster response research and practice. The results will extend the GeoAI knowledge and its critical role in disaster response, as well as underscore prospects for future research and practice in this domain.

For more:

Zou, L., Mostafavi, A., Zhou, B., Lin, B., Mandal, D., Yang, M., Abedin, J. and Cai, H., 2023. GeoAI for Disaster Response. In Handbook of Geospatial Artificial Intelligence (pp. 287-304). CRC Press.

Figure 1. Handbook of Geospatial Artificial Intelligence

Figure 2. GeoAI for Disaster Response

Congratulations! We look forward to hearing more achievements from GEAR Lab! Gig'em!



Geospatial Exploration and Resolution (GEAR) Lab

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