AI and Space

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Imagination will often carry us to worlds that never were. But without it we go nowhere.Carl Sagan*

Crop Prediction

To analyze these images, however, the data needs proper classification. To solve this problem, Descartes Labs, a data analysis company, stitches together daily satellite images into a live map of the planet’s surface and automatically edits out any cloud cover.* With these cleaned-up images, they use deep learning to predict more accurately than the government the percentage of farms in the United States that will grow soy or corn.* Since the production of corn is a business worth around $67B, this information is extremely useful to economic forecasters at agribusiness companies who need to know how to predict seasonal outputs. The US Department of Agriculture (USDA) provided the prior benchmark for land use, but that technique used year-old data when released.

Figure: A picture of the yield forecast of different areas of the United States.

In 2015, for example, the FDA predicted a domestic production of 13.53 billion bushels of corn. Descartes Labs, however, forecasted 13.34 billion bushels, as seen in the picture above. Descartes Labs used an almost live view to visualize and measure developments such as floods or changes in crop condition. Using deep learning, the company exploited data from NASA and other sources and analyzed it faster than the government, predicting future yields based on the data collected.

The government spent endless resources surveying farmers across the country to identify the existing crops for each commodity in order to predict future yield. Descartes Labs eliminated this burden, reducing the cost of predicting the harvest. They trained their algorithm, which extracts valuable information from the satellite imagery, to predict future corn crops based on the color and appearance of the plants in the field.

And, this is just the beginning of extracting information from satellite images. Other startups are looking at different use cases. For example, Orbital Insight uses deep learning to scrutinize infrastructures, such as parking lots and oil storage containers, to predict and reveal important economic data.

Finding Planets

Deep learning has not only been helpful in analyzing Earth, but also in discovering what is in the universe. With eight planets orbiting the Sun, our solar system held the title to the most planets around a star in the Milky Way galaxy. But in December 2017, NASA and Google discovered a new planet orbiting a distant star, Kepler 90, bringing the total number of planets for that star to eight as well. That discovery was no easy feat considering that the star is located over 2,500 light years away from us.

Using a telescope that has been searching for planets since 2009, NASA’s Kepler Telescope, scientists have discovered thousands of planets. Today’s difference is that instead of astrophysicists manually finding new discoveries, neural networks do the work.

Figure: Brightness* drop of the star.*

NASA’s Kepler Telescope shows data with the brightness of a star, based on images taken from the telescope. A planet can be spotted based on the change in the star’s brilliance. When a planet circles a star and passes between the star and telescope, it blocks some of the light the star emits. Based on the drop in brightness, it is possible to determine if a planet is circling a star. A planet shows up as a pattern that repeats every orbit as Earth’s view of the star is obscured. With that in mind, researchers defined a neural network to identify planets around a star.

This technique found two different planets around two separate star systems. The researchers plan to use the same method to explore all 150,000 stars that Kepler’s telescope has data on. This frees astrophysicists to research other areas since they do not need to look for a needle in a haystack, manually looking at every image to find patterns. A neural network does the work for them. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves,” stated Christopher Shallue.*

Additional Developments

But these developments only scratch the surface. Deep learning broadens the horizon for the potential in space exploration. For example, at the International Space Station, Airbus’s small robot CIMON, Crew Interactive Mobile Companion, talked to German astronaut Alexander Gerst for 90 minutes on November 15, 2018.* Gerst used a language much like that used with voice assistants: “Wake up, CIMON.” CIMON is a very early demonstration of AI being used in space.*

Most people today rely on GPS to locate themselves, but that does not exist in space. So, NASA and Intel teamed up to solve problems of space travel and colonization using AI.* Intel hosted an eight-week program focused on this effort. One of the nine teams developed a tool to find one’s location in space by training a neural network to identify the position where a photo is taken. It trained a neural network to do so using millions of actual images as the training data.

So, today we concentrate on Earth and areas that we are familiar with, but space is vast with unlimited possibilities. Currently, over 57 startups exist in the space industry, focusing on areas such as communication and tracking, spacecraft design and launch providers, and satellite constellation operation.* This represents an enormous upsurge from 2012, which saw little funding and few dedicated companies.