Through this project, we aim to provide enhanced intelligence and autonomy to the aerial vehicles and small satellites in Earth monitoring applications using technologies such as artificial intelligence, embedded systems and optimized high-performance computing. The project is mainly funded through the IKTPLUSS-IKT and digital innovation framework at the Norwegian Research Council (webpage at the NRC). The project period is 2022 - 2027.

Remote sensing satellites and aerial vehicles are nowadays indispensable tools for Earth monitoring and for improving the predictability of Earth system processes. In particular, small satellite technology is characterized by lower development costs, low-power electronics, affordable launch opportunities and technology demonstration. A similar trend has been observed in Unmanned Aerial Vehicle (UAV) system development due to rapid industry expansion and the miniaturization trend of imagers and sensors. These trends have enabled agents to be equipped with advanced instruments such as multispectral and hyperspectral imaging systems. Using Hyperspectral imaging onboard a satellite or UAV, however, is challenging due to large information content, limited processing time, power and data downlink.

ARIEL will contribute to fast decision making by advancing onboard processing on small satellites and aerial vehicles for persistent Earth observation. The planned outputs will contribute toward reaching the goals of the international conventions on sustainable development.

NTNU will collaborate with industry players who ensure that the research is driven by the industry's needs. The research on the implementation of algorithms for forest monitoring will be performed together with S[&]T, whereas algorithms for ship detection application will be explored together with VAKE. ARIEL will be supported by Xilinx to build solutions that will ensure the processing fulfils the real-time constraints, whereas Inventas will help in the development of a verification framework. The project will extensively use the hyperspectral data provided by HYPSO-1 CubeSat built at NTNU and launched in January 2022.

Project participants

Project leader: Assoc. prof Milica Orlandic, Dept. of Electronic Systems

Key personnell

Samuel Boyle, Dept. Electronic Systems, PhD candidate
Cameron Penne, Dept. Electronic Systems, PhD candidate