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ESA’s Φ-Week 2020 Highlights Digital Twin Earth, AI, and Quantum Computing

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September 29, 2020
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This year’s ESA’s Φ-week event started on September 28 and would last until October 2. It showcases a series of stimulating speeches about Digital Twin Earth, an update on Φ-sat-1, and an exciting novel initiative that involves quantum computing.

ESA’s 2020 Φ-week gives people to connect and form networks with experts, scientists, educators, students, developers, global industries, start-ups, and institutions in the field of space. It aims to explore the latest applications of transformative technologies and inspire early-career scientists, citizens, entrepreneurs.

Digital Twin Earth

The Φ-week event goes virtual this year and focuses on how Earth observation contributes to Digital Twin Earth. The Digital Twin Earth provides a precise representation of Earth’s past, present, and future changes.

Through Digital Twin World, human and nature activity on the planet will be visualized, monitored, and forecasted. Digital Twin Eart will monitor the Earth’s health and conduct simulations of the interconnected system of Earth with human behavior, and support viable development that reinforces Europe’s efforts for a better environment in response to Green Deal‘s the urgent challenges and targets.

Experts will discuss the concept, practical implementation, and infrastructure of the Digital Twin Earth and exhibit insights on the way industries. The community contributes to making the project successful during the ESA’s 2020 Φ-week.

Updates on Φ-sat-1

On September 3, the first artificial technology (AI) was launched onboard the European Earth Observation Mission. The Φ-sat-1 is the first of its kind and the first experiment in improving the efficiency of sending vast quantities of data back to Earth.

ESA and cosine remote sensing are delighted to reveal on the first day of ESA’s Φ-week event that the Deep Convolutional Neural Network has performed the first-ever hardware-accelerated artificial intelligence inference Earth observation images on an in-orbit satellite. It was the University of Pisa that developed the Deep Convolutional Neural Network.

The ɸ-sat-1 was successful in prefiltering Earth observation data. Only the essential usable part of the image is downlinked to the ground, which improves the bandwidth utilization and significantly reduces the aggregated costs of the downlink.

<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/JwfSYKYNLXg” frameborder=”0″ allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture” allowfullscreen></iframe> 

Initial data coming from the satellite showed that that the automatic cloud detection algorithm operated by the AI has correctly filtered hyperspectral Earth observation imagery from the sensor of the satellite into cloudy and non-cloudy pixels.

Read Also: Watch! Latest Flyover Footage From ESA’s Spacecraft Shows Stunning View of Ice-Filled Crater of Mars

Quantum Computing

As mentioned in the opening speech, the novel initiative involving quantum computing exploits quantum phenomena like superposition, entanglement, and tunneling to improve performance, decrease computational costs, and solve intractable problems in Earth observation.

The novel initiative uses artificial intelligence to support programs like Digital Twin Earth and Corpenicus in creating a quantum capability that can solve the demanding Earth observation problems. Quantum computing will be developed at ESA’s Φ-lab at ESA’s center for Earth observation in Italy that embraces transformational observation.

ESA and CERN collaborated on many projects before, and this will be extended to the CERN Quantum Technology Initiative announced last June this year by Fabiola Gianotti, CERN Director-General.

Through this, both ESA and CERN will make new synergies and build on their shared experience in data mining, big data, and pattern recognition.

Read More: NASA/ESA’s Hubble Captures Images of Cygnus Supernova Blast

Check out for more news and information on ESA at Science Times.

©2017 ScienceTimes.com All rights reserved. Do not reproduce without permission. The window to the world of science times.



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