Geospatial AI

Cray Solutions for Geospatial Object Detection

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The Big Potential of Big Geospatial Data

Geospatial data has evolved. Once accessible only to government agencies and the largest of corporations, its applications were limited. But times and technologies change.

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We've been helping visionaries like you solve the most complex computing problems for over four decades.

Today, geospatial data is widely available. And with increasing access to it has come increasing use cases. Industries as varied as agriculture and healthcare can derive actionable intelligence and business value from the ability to analyze data with geographic or locational components. But there’s a big difference between “can” and “do.”

Extracting insight from geospatial data comes with distinct challenges. The ability to apply artificial intelligence techniques like deep learning is critical to improving the speed and accuracy of results. However, converting raw geospatial data into real-world applications demands a unique computing and storage architecture.

ARTIFICIAL INTELLIGENCE IN GEOSPATIAL ANALYSIS

Why Your Success in Geospatial Object Detection Depends on How Well You Apply Deep Learning

Recognizing multiple, isolated objects in digital images and videos is an incredibly difficult undertaking. It involves multiple types of data derived from an array of sensing systems and cameras. Format consistency, value ranges, and integration are key variables which impact the speed and effectiveness of geospatial object detection workflows.

Deep learning has emerged as a promising way to improve computer-driven visual recognition tasks. In the geospatial industry, deep learning is making the process of identifying features or objects in detailed imagery ― and delivering information you can use ― easier. The availability of deep learning tools has enabled the use of AI to execute this labor-intensive task.

But with the rapidly compounding growth rate of datasets and the demands of advanced analytics and AI workflows, you must have the right compute and storage capabilities.

The AI workflow for geospatial object detection needs a computing architecture comprised of heterogeneous compute nodes, hybrid storage, and software that supports AI. Cray’s geospatial reference architecture addresses these requirements of this sector by providing a highly flexible and effective way to apply advanced analytics and AI methods to new problems involving geospatial data.

How to Solve Workload and Data Management Challenges in Geospatial AI

Deep learning is transforming geospatial data applications ― and enabling organizations to dramatically improve the speed and accuracy of results. Learn how to make the most of it.



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CRAY SOLUTIONS
FOR GEOSPATIAL AI

Deep Learning-Driven Geospatial
Object Detection for Real-World Insight

Explore the industry trends accelerating geospatial data usage and compute and storage requirements for the geospatial object detection AI workflow.

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The Cray® CS500™ system is customizable, easy to manage, and based on industry standards. It can handle the broadest range of modeling, simulation, analytics, and artificial intelligence workloads. And it’s configured to meet your needs.

Built for speed, scale, and performance – and optimized for geospatial AI workflows – the Cray® CS-Storm™ cluster system simplifies your process, reduces risk of costly trial-and-error, and easily scales to keep up as your needs grow.

Fast, scalable ClusterStor storage meets all the requirements of the storage challenge for autonomous driving. It embeds the Lustre® file system in an engineered HPC storage system and incorporates the industry’s highest-performing and most efficient data storage technologies. The result is maximum storage performance at the lowest overall TCO.

The Cray® Urika®-CS AI and Analytics software suite gives you open-source frameworks and tools for machine and deep learning, backed by Cray expertise and support. Integrated into ready-to-run containers, the Urika-CS suite helps you take advantage of the performance and scale of our CS series systems.

WHY CHOOSE CRAY

Cray has been solving the most complex computing problems in science and industry for over 45 years. Organizations big and small trust Cray experts. We’ll work closely with you to get you on the right path for your autonomous driving goals and then we’ll stay with you to make sure you have what you need to succeed.

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