To present a paper in the session, you will first need to register and submit your abstract online (http://www.aag.org/annualmeetings/), and then email your presenter identification number (PIN), paper title, and abstract to one of the organizers listed below by October 20, 2018.
Zhenlong Li, Department of Geography, University of South Carolina, US. email@example.com
Qunying Huang, Department of Geography, University of Wisconsin-Madison, Madison, US. firstname.lastname@example.org
Wenwu Tang, Department of Geography and Earth Sciences, University of North Carolina at Charlotte,US. email@example.com
Eric Shook, Department of Geography, Environment, and Society, University of Minnesota, US. firstname.lastname@example.org
Qingfeng Guan, School of Information Engineering, China University of Geosciences, China. email@example.com
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 propagation of smart devices and social media also provide extensive geo-information about daily life activities. Efficiently analyzing those geospatial big data streams enables us to investigate unknown and complex patterns and develop new decision-support systems, thus provides unprecedented values for business, sciences, and engineering.
However, handling the "Vs" (volume, variety, velocity, veracity, and value) of big data is a challenging task. This is especially true for geospatial big data since the massive datasets often need to be analyzed 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/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.
• Geospatial big data processing, mining and visualization methods using high-performance computing and artificial intelligence.
• Integrating scientific workflows in cloud computing and/or high performance computing environment.
• Any other research, development, education, and visions related to geospatial big data computing.
|Presenter||Mbongowo Mbuh*, Department of Geography and Geographical Information Science, University of North Dakota, Peter Brandt, Department of Geography and Geographical Information Science, University of North Dakota., Application of real-time GIS cloud-based context-aware information services for local government emergency preparedness in the era of big data for a resilient and smart city management||20||5:00 PM|
|Presenter||Jian Chen*, University of North Alabama, Satya Katragadda, Informatics Research Institute, University of Louisiana at Lafayette, Shaaban Abbady, Center for Advanced Computer Studies, University of Louisiana at Lafayette, MapReduce Based Spatial Hotspots Detection Using Polygon Propagation||20||5:20 PM|
|Presenter||Liem Tran*, University of Tennessee at Knoxville, Big Data and the Old Scale Issues||20||5:40 PM|
|Presenter||Wenwu Tang*, University of North Carolina at Charlotte, Minrui Zheng, University of North Carolina at Charlotte, Zachery Slocum, University of North Carolina at Charlotte, Jianxin Yang, University of North Carolina at Charlotte, Craig Allan, University of North Carolina at Charlotte, A Cyberinfrastructure Approach for Big Data-Driven Microtopographic Analysis||20||6:00 PM|
|Presenter||Hai Lan*, University of Maryland, Utilizing a Spark-Based Cloud Computing Framework to Address Big Remote-Sensing Data Processing: Multi-scale Large Landsat 8 Dataset and MODIS as an Example||20||6:20 PM|
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