Meet

The Instructor

Dr. Raúl  Guerra

Bio

Raul Guerra received the Electrical Engineering degree from the university of Las Palmas de Gran Canaria, Spain, in 2012, the master’s degree in telecommunications technologies from the Institute of Applied Microelectronics, and the Ph.D. degree in telecommunications technologies from the University of Las Palmas de Gran Canaria, in 2017. He was funded by the University of Las Palmas de Gran Canaria to do his Ph.D. research in the Integrated System Design Division. In 2016, he was a Researcher with the Configurable Computing Lab, Virginia Tech University.  In 2018 he was awarded the prize for the best Ph.D. Thesis in remote sensing finished during 2017 in Spain by the IEEE GRSS. Currently, he works as a postdoctoral researcher in the University of Las Palmas de Gran Canaria where he also teaches image processing and electronics. His research interests include the development of algorithms for images processing and their hardware implementation.

Lecture content

Introduction to GPU Parallel Model to Speed up High Computationally Demanding Tasks (related to Image Processing and Remote Sensing)

In this session a general overview of a generic GPU parallel model will be first provided. Then, we will go into the details more closely related to NVIDIA GPU architecture that will be useful for better understanding the key aspects of CUDA programming model. A basic introduction to CUDA programming model will be provided, coupled with some examples specifically related to image processing and remote sensing. These examples will be used as starting point in the second session.

Lecture content

Remote Sensing Example. UAV Application Integrating NVIDIA Low Power GPUs (Jetson Nano, Jetson Xavier NX)

In this session a particular use of NVIDIA LPGPUs for remote sensing applications will be shown. It targets an specific application were a NVIDIA developer kit (Jetson Nano or Jetson Xavier NX) is used as on-board computer within an UAV. On one side, the on-board computer is used to control the flight mission and the hyperspectral data acquisition. On the other side, the on-board computer is also used to compressed the acquired hyperspectral frames in real time. The goal is to first provide a general overview of the application, its requirements and limitations. Then the solution that have been developed will be shown, paying special attention to the CUDA implementation of the compression algorithm, as well as the parallelization strategies used.


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Meet

The Instructors

Dr. May Casterline

Bio

Dr. May Casterline is a data scientist/image scientist/software developer with a background in satellite and airborne imaging systems. Her research interests include deep learning, hyperspectral and multispectral imaging, innovative applications of machine learning approaches to remote sensing data, multimodal data fusion, data workflow design, high performance computing applications, and creative software solutions to challenging geospatial problems. She holds a PhD and Bachelors of Science in Imaging Science from Rochester Institute of Technology, with a focus on remote sensing. In industry she has acted as a product owner, technical lead, lead developer, and image scientist on both research initiatives and development projects.  As a Senior Solutions Architect at NVIDIA, Dr. Casterline works with both industry and government to help enable developers, engineers, data scientists and analysts integrate artificial intelligence and GPU-accelerated solutions into their workflows and products.

Alison Lowndes

Bio

Alison Lowndes (Senior Scientist, Global AI | NVIDIA). Joining in 2015, Alison spent her first few years as a Deep Learning Solutions Architect and is now responsible for applied Artificial Intelligence both around the globe and off Earth, in Space. A mature graduate in AI, Alison combines technical and theoretical computer science with astrophysics & over 25 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of applications, including planetary defence with NASA, ESA & the United Nations and works closely with world governments advising them on how to harness AI for economic growth, national security & climate action, using NVIDIA’s GPU Computing platform.

Lecture content

(1) Fuelling the AI Revolution with Gaming and (2) Accelerating Geospatial workloads with GPU

This talk will be an introduction to NVIDIA and the vital tools necessary for remote sensing in the Artificial Intelligence (AI) & virtual world era. NVIDIA invests both in internal pure research and platform development to enable a diverse customer base, across gaming, VR, AI, robotics, simulation, digital twinning, graphics, real-time rendering, autonomy, HPC & more. You will be introduced to the hardware and software platform at the heart of this; NVIDIA GPU Computing & will gain insights into how academia, enterprise and startups are applying AI, deploying it in Space as well as designing and testing in readiness. You will also gain a glimpse into state-of-the-art research from world-wide laboratory collaborations & internal work at NVIDIA, demoing, for example, how to illuminate the Moon's craters. Finally this talk will provide a deeper dive into the workflow from satellite to insight. Beginners might like to try some of our classes using GPU’s in the cloud: www.nvidia.co.uk/dli (codes available on request).

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