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Deep learning to explore the dark areas of the moon - Actu IA

#artificialintelligence

NASA's Artemis program aims to send astronauts to the south pole of the Moon, where water in the form of ice has been confirmed, rather than near the equator as with the Apollo mission. The dark areas are likely to contain more ice than the others but also to be dangerous for the astronauts venturing there. A team of researchers studied these areas using deep learning, the study entitled "Cryogeomorphic Characterization of Shadowed Regions in the Artemis Exploration Zone" was published in Geophysical Research Letters. For the first Artemis lunar missions, the selected astronauts (one man and one woman) will fly to the south pole of the moon. This region has a great potential, it is thought to have the greatest abundance of water ice because it has craters where the sun's rays never penetrate, their temperature is estimated at -170 .


Artificial Intelligence provides sharper images of lunar craters that contain water ice

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The moon's polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany present the highest-resolution images to date covering 17 such craters. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1–2 meters per pixel, which is at or very close to the best capability of the cameras.


Peering into the Moon's shadows with AI

#artificialintelligence

The Moon’s polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany presents the highest-resolution images to date covering 17 such craters in the journal Nature Communications. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the Moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1-2 meters per pixel, which is at or very close to the best capability of the cameras.


A Summer of Space Exploration with Intel and NASA - Intel Nervana

@machinelearnbot

This summer, Intel has been collaborating with the NASA Frontier Development Lab (FDL), an AI R&D accelerator targeting knowledge gaps useful to the space program. The NASA FDL, hosted at the SETI Institute, was established to apply AI to five specific challenges in areas relevant to the space program: Planetary Defense (defending the Earth from potentially hazardous asteroids), Space Weather (better predicting solar activity) and Space Resources (locating and accessing the resources we'll need to go back to the moon and expand into the solar system). Earlier this summer, we introduced you to this collaboration, and we have exciting updates to share. The NASA FDL team successfully applied the Intel Nervana Deep Learning platform to automate the creation of lunar maps at our Moon's poles – a critical step in helping both identify potential landing sites and navigation in the shadowed regions of the moon. Here, permanent darkness and extremely low temperatures make for an ideal location for water ice (and other volatiles), but highly challenging conditions for future missions that would be impossible without detailed mapping.