Satellite imaging swarms with distributed onboard intelligence

Space-based monitoring systems for Earth observation are being increasingly employed by industry and governments, and large constellations having tens to hundreds of units will be deployed in the near future. This project considers swarms of satellites in formation flight, and it investigates innovative cooperation modalities to enhance image quality and enable satellite distributed onboard inference. Through artificial intelligence (AI), multiple simultaneous and redundant satellite image acquisitions from different viewing angles will be cooperatively combined into scene representations having significantly higher spatial and spectral resolution and augmented with surface profile information, enabling imaging beyond the diffraction limit by using very high-resolution training sets. These representations will be computed onboard the satellites in a distributed fashion and will be used for inference and image reconstruction; they will allow to increase quality, availability and timeliness of the data and to optimize the usage of inter-satellite and satellite-to-ground communication links. Enabling this cooperation requires major scientific advances; the project will generate new knowledge via innovative scene models allowing spatial/spectral resolution enhancement and 3D augmentation, innovative AI-based compression techniques, novel distributed inference methodologies for AI, as well as a new generation of fast and energy-efficient AI models powering distributed onboard computing. This ambitious research will become the cornerstone of next-generation satellite imaging and inference systems, increasing scientific and economic value for the users and allowing public and private entities to exploit advanced image products and analytics that can benefit the economy and the society. The developed methods will also be relevant to several other types of embedded systems, further extending the project impact to areas such as sensor networks and Internet of Things.