Devin White, Senior Researcher at Oak Ridge National Laboratory shares how they are using GPUs to improve the geolocation accuracy of imagery collected by a satellite, manned aircraft, or an unmanned aerial system.
Using Tesla K80 GPUs and CUDA, the researchers in the Geographic Information Science and Technology Group at ORNL developed a sensor-agnostic, plugin-based framework to support photogrammetric and computer vision processing tasks like image registration and orthorectification.
Read about Devin’s work in more detail on his recent GPU Computing Spotlight interview on Parallel Forall.
Share your GPU-accelerated science with us at http://nvda.ly/Vpjxr and with the world on #ShareYourScience.
Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ly/X7WpH
Share Your Science: Improving the Geolocation Accuracy of Aerial and Orbital Imagery with GPUs
Sep 07, 2016
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AI-Generated Summary
- Researchers at Oak Ridge National Laboratory developed a sensor-agnostic, plugin-based framework for photogrammetric and computer vision processing tasks like image registration and orthorectification.
- The framework uses Tesla K80 GPUs and CUDA to improve geolocation accuracy of imagery from satellites, manned aircraft, and unmanned aerial systems.
- Devin White, Senior Researcher at ORNL, detailed the GPU-accelerated approach in a recent GPU Computing Spotlight interview.
Next Steps
- Read the GPU Computing Spotlight interview on Parallel Forall for more details on the research.
- Share your GPU-accelerated science at http://nvda.ly/Vpjxr and with the world on #ShareYourScience.
- Watch more scientists share how accelerated computing benefits their work at http://nvda.ly/X7WpH.
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