Recording Plan: 

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Call For Participation
We invite presentations to this session that has been organized for seven years. To present a paper in this session, please submit your abstract online by Oct. 31, 2024 (https://www.aag.org/events/aag2025), and email your abstract code/PIN, paper title, and abstract to one of the following organizers by Dec. 2, 2024.

Description:

Aims and Scope:
The convergence of big data and spatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. Earth observation systems and model simulations are generating massive volumes of disparate, dynamic, and geographically distributed geospatial data with increasingly finer spatiotemporal resolutions. Meanwhile, the ubiquity of smart devices, location-based sensors, and social media platforms provide extensive geo-information about daily life activities. Efficiently analyzing those geospatial big data streams enables us to investigate complex patterns and develop new decision-support systems, thus providing unprecedented values for sciences, engineering, and business. However, handling the “Vs” (volume, variety, velocity, veracity, and value) of geospatial big data is a challenging task as they often need to be processed, analyzed, and visualized in the context of dynamic space and time. This section aims to capture the latest efforts on utilizing, adapting, and developing new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges for supporting geospatial applications in different domains such as climate change, disaster management, human dynamics, public health, and environment and engineering.
Potential topics include (but are not limited to) the following:
• Geo-cyberinfrastructure integrating spatiotemporal principles and advanced computational technologies (e.g., high-performance computing, cloud computing, and deep learning/GeoAI/Generative AI).
• New computing and programming frameworks and architecture or parallel computing algorithms for geospatial applications.
• New geospatial data management strategies and data storage models coupled with high-performance computing for efficient data query, retrieval, and processing (e.g., new spatiotemporal indexing mechanisms).
• New computing methods considering spatiotemporal collocation (locations and relationships) of users, data, and computing resources.
• Big data supported geograhpic complex adaptive systems, including theory, practice, modeling, and monitoring.
• Geospatial big data processing, mining, and visualization methods using high-performance computing and artificial intelligence.
• Other research, development, education, and visions related to geospatial big data computing.


Organizers:
• Zhenlong Li, The Pennsylvania State University, US. zhenlong@psu.edu
• Huan Ning, The Pennsylvania State University, US. hmn5304@psu.edu
• Qunying Huang, University of Wisconsin-Madison, Madison, US. qhuang46@wisc.edu
• Eric Shook, Environment, and Society, University of Minnesota, US. eshook@umn.edu
• Wenwu Tang, University of North Carolina at Charlotte, US. wenwuTang@uncc.edu

We also have another similar section:
Big Data Computing for Geospatial Applications
https://aag.secure-platform.com/aag2025/solicitations/82/sessiongallery/23116

Non-Presenting Participants