Third Party Interferences (TPIs) are responsible for a large part of all failures in transmission pipelines. Examples of TPIs are excavations, construction activities, dredging, etc.. An efficient monitoring service for (short lived) TPIs requires high resolution data, a high revisit rate and needs to be dependable. These requirement cannot be filled by any single satellite mission alone. A feasibility study is proposed to investigate the quality of detection of TPIs when combining Sentinel-1 images at a high revisit frequency with higher resolution data acquired with a low revisit frequency. The results of this study will be integrated in the existing Orbital Eye infrastructure for pipeline integrity monitoring. This will result in a system that is fully automatic and continuously operational, can monitor both long and short lived TPIs and has an operational cost that is considerably lower compared to existing solutions. No such system currently exists on the European market, creating a unique business opportunity.

The mission of RefMap is to develop a digital service aimed at quantifying the environmental footprints of air mobility for airliners and unmanned aircraft systems (UAS) at a "multi-scale" level, where single-trajectories (micro) and the flow traffic of multiple vehicles (macro) are optimised to minimise their environmental impact in a wide range of communities. RefMap investigates how the aviation business models will be affected by the availability of environmental data for each type and route of air vehicle, as this will enable stricter evidence-based Green policy making in the sector. This will be achieved via the development of the RefMap analytics platform processing environmental and weather data such as wind, noise, CO2 and non-CO2 emissions for both U-space and ATM. This platform will rely on a number of technical solutions, including numerical simulation, predictive models, and deep-learning methods. The latter will be used to construct accurate non-intrusive prediction frameworks and to optimize the trajectories of the various vehicles given the predicted flow conditions via deep reinforcement learning (DRL). These will enable the development of a new aviation business models aligned with EU’s Green Agenda.