AI in Space in High Detail Speeding up the Processing of Space Images

Much of the information that is beamed back from space is useless. Pictures taken by satellites orbiting the Earth might take days to download, only to show lots of cloud obscuring the area of interest. The subject matter may also be surrounded by irrelevant information. All this uses up a lot of valuable bandwidth.考生如果怕自己錯過考試報名時間和考試時間的話，可以 免費預約短信提醒，屆時會以短信的方式提醒大家報名和考試時間。

Processing data in space, before transmission, would reduce clutter, but this can be tricky. Cosmic rays randomly flip the ones and zeroes that computers operate on, introducing unpredictable errors. High levels of radiation can also damage electronic circuits. KP Labs, based in Gliwice, Poland, is building a satellite to overcome some of these problems. Their device, called Intuition-1, is controlled by a neural network, a form of artificial intelligence modelled on the human brain. The satellite is what is known in the trade as a 6U CubeSat, which means it is composed of six standard-sized 10x10x11.5cm modules.

Intuition-1 will be equipped with a hyperspectral imager, which takes 150 pictures of every scene it looks at. Each picture is at a different spectral frequency, so contains different information. The neural network stitches these together using powerful graphics chips hardened against radiation. The developers have also built error correction into their software.

Intuition-1 will view a 15km-wide swathe of Earth at a resolution of 25 metres per pixel. This will be able to reveal details such as how well crops are growing or allow the number of trees in a forest to be counted.

But instead of transmitting back every last bit of image data, the satellite will summarise what the user requests as useful information. This might, for instance, be a heat-map showing areas of weeds in a field or the location of a forest fire. Reducing the data load means that some of this information can be transmitted live.

The satellite will be used to prove that a hardened neural network can survive in space. This could pave the way for other space applications. For example, the Curiosity rover on Mars was successfully upgraded in 2016 with a set of algorithms to detect “interesting” rocks for investigation, instead of picking them randomly. A neural network could provide future rovers and deep-space probes with a better ability to make decisions.

The neural network and hyperspectral imager have already been built and tested by KP labs. The kit will go into a satellite body being constructed by Clyde Space, a satellite producer based in Scotland, and launched in 2022. After that there will be more intelligence in space.

太空AI高處見微加速太空圖像處理

從太空傳回的信息大多是無用的。在地球軌道上運行的衛星所拍攝的照片可能要花好幾天才能下載回地面，結果卻只看到目標區域被重重云層遮蓋。主要內容還可能伴隨著大量無關緊要的信息。這一切耗費了大量寶貴的帶寬。

傳輸前在太空先行處理數據可以減少無用信息，但操作起來有難度。宇宙射線會導致計算機二進制信息的1和0發生隨機翻轉，導致不可預測的錯誤。強輻射也可能損壞電路。位于波蘭格利維采(Gliwice)的KP實驗室(KP Labs)正在打造一顆衛星來克服其中一些問題。這顆衛星名為“直覺一號”(Intuition-1)，由一個神經網絡(一種模擬人腦的人工智能形態)控制。按業內的說法，這是一種6U立方衛星(6U CubeSat)——由六個10x10x11.5cm的標準尺寸模塊組成。

“直覺一號”將配備一臺高光譜成像儀，對每一目標場景拍攝150張照片，每張照片的光譜頻率不同，因而包含不同信息。衛星內的神經網絡運用經抗輻射加固的圖形芯片將照片拼合起來。開發人員還在軟件中加入了糾錯功能。

“直覺一號”將以每像素25米的分辨率拍攝15公里范圍內的地球圖像。這能夠顯示諸如作物長勢或森林樹木數量等細節。

但該衛星并不會把圖像數據巨細靡遺地傳回地球，而是提煉出用戶所請求的有用信息，例如顯示農田內的雜草區域或森林火災位置的熱點圖。數據量減少意味著一些信息可被實時傳輸。

該衛星將用于證明一個經加固的神經網絡能在太空持續使用。這可以為其他太空應用鋪平道路。例如，火星上的好奇號探測車在2016年成功升級，運用一套算法來挑選“有趣”的巖石進行研究，而不再是隨機選取。神經網絡可為未來的星球探測車和深空探測器提供更好的決斷力。

KP實驗室已制造并測試了上述神經網絡及高光譜成像儀。該套件將安裝到由蘇格蘭衛星制造商Clyde Space制造的一顆衛星上，于2022年發射。往后，太空將變得更智能。

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