The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

Image coregistration aims at stacking two or multiple images in a way such that, for each image, the same pixel corresponds to the same point of the target scene (possibly with sub-pixel accuracy). We can distinguish two families of image coregistration problems, basically depending on if the images to be coregistered are taken by sensors of the same or different type (e.g., sensing different wavelenghts), and with similar or different illumination and acquisition geometries (e.g. different sun illumination conditions and/or different acquisition incidence angles). Whilst the first type of image coregistration is well established, multimodal coregistration is not yet well founded and due to difficulty of finding correspondences between the images (tie points) in a robust way, and the avable approaches often recur to manual assistance. The multimodal image coregistration technique proposed in this work overcomes the problems due to differences in radiometries and in geometries by exploiting two main concepts: complementary geometry information between the images to be coregistered, and mutual information (or entropy) as similarity metric. The method focuses on coregistration of very high resolution synthetic aperture radar (SAR) and optical images, but the approach is of general validity. The tests performed on real very high resolution optical and SAR data confirm the validity of the method.