Satellites have become an essential part in our lives: from navigation and weather forecasting to broadcasting and disaster management. They acquire enormous amounts of data which are sent back to Earth for further processing and analysis. Cost-effective downlink with high throughput is usually not available, especially for small satellites. Although satellites are collecting large amounts of data from various sensors, the downlink capacity is not sufficient enough to bring data to Earth in a timely and cost-effective manner. Another problem is limited resources on satellites (power, space, memory, etc.) and their usage has to be carefully designed and optimized. However, space environment can be very unpredictable and not easily simulated on Earth. Constant auto-optimization by “learning” from the measured data could help to utilize the resources in the optimal way.
The above mentioned problems can be solved with an AI module with onboard processing capability. For example, if a satellite is monitoring objects of interest on the ground, defocused or cloud-filled images are useless and do not need to be downstreamed. Moreover, objects of interest could be detected and cropped or parameterized from images onboard (e.g. regions of interest like agricultural land, urban regions, etc.), further reducing the amount of data for downstreaming. Another example is battery usage optimization where an onboard module could monitor battery power and learn how to reduce energy consumption or manage power distribution, prolonging the designed lifetime. Many of these problems could be solved by AI algorithms that would filter out unwanted images and would reduce the amount of data that needs to be downlinked or by optimizing satellite behavior like energy consumption, navigation, etc.