A new press release reports, “Today at the 2019 International Supercomputing Conference in Frankfurt, Germany, global supercomputer leader Cray Inc. announced enhanced capabilities to empower data scientists and engineers who are innovating in the field of Geospatial AI. Cray introduced a new Geospatial Reference Configuration as well as new features in its Cray® Urika®-CS and Urika®-XC AI and Analytics software suites. The new features include an augmented Deep Learning Plugin that provides best-in-class deep neural network performance training and broadened support for deep learning frameworks. In performance studies, the plugin showed training time reductions up to 23% over open source alternatives for a single node, dense GPU configuration. Both the reference configuration and plugin are designed for IT and AI teams implementing complex infrastructure to support Geospatial AI workloads. Cray also announced that it has delivered and installed a Cray CS™ Series system at the U.S. Geological Survey agency to support AI initiatives in geospatial analysis and the agency’s mission to provide reliable information for understanding the Earth.”
The release goes on, “Geospatial AI is the marriage of geospatial data and artificial intelligence. It promises to be one of the most important uses of AI across a range of industries such as oil and gas companies, state and local governments, property and casualty insurance businesses, weather forecasting centers, and beyond. Data scientists are exploring the use of AI, deep learning and machine learning to deliver new applications and insights based on geospatial data. For example: (1) Oil and gas companies will perform market supply analysis by applying AI to satellite images of tank farms and refineries. (2) Municipal governments will use AI to detect changes in satellite imagery for infrastructure planning and disaster and resiliency response planning. (3) Property and casualty insurance businesses will apply AI to satellite imagery for disaster impact analysis and claim fraud detection. (4) Weather forecasters will make more accurate predictions because Geospatial AI uncovers new information, such as soil moisture, with high resolution.”
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