Globally, there is increased concern of the potential impacts of extreme climate events and their impact on loss and damage of people, assets and property as a result of these events. Therefore, natural partners in using climate services to assess risk are the Global Insurance Sector, who are key implementers in increasing societies resilience and recovery of extreme events and who are integral, co-design partners in this programme. This project intends to operationalize a system, called the Oasis Loss Modelling Framework, that combines climate services with damage and loss information and provides a standardised risk assessment process that can assess potential losses, areas at most risk and quantify financial losses of modelled scenarios. We intend to prove the Oasis LMF system through undertaking a range of demonstrators linked and co-designed to ‘real’ situations and end-user communities in the insurance, municipalities and business sectors (see list of partners & collaborators). These demonstrators have already been agreed with our end-users and develop work around hydro-climatic risk (in the Danube Region), Typhoon Risk, African Farmer Risk – through using climate information to support the underwriting of micro-insurance, climate v health and climate v forest asset risk assessment. We also intend to further expand access by all sectors to the models, tools and services developed within this programme and the broader climate services sector by operationalizing an open eMarket place and matchmaking facility for catastrophe and climate data and models, tools and services and through broadening awareness in the climate modelling and end-users communities to the Framework, and the transparent and comparable standard it offers to support evidence based risk assessment and adaptation planning.

In the last years irregular migration has dramatically increased, and is no longer manageable with existing systems. Improved methods for border surveillance are necessary to ensure an effective and efficient EU border management. FOLDOUT focus is on through foliage detection in the inner and outermost regions of the EU. Foliage penetration is an unsolved important part of border surveillance. By solving the problem of unreliable detections in such harsh environments border guards’ workloads are reduced, costs are reduced and, last but not least, lives can be saved. Detecting people through dense foliage in extreme climates with only a penetration technology is prone to high fault rates. FOLDOUT will build a system that combines various sensors and technologies and intelligently fuses these into an effective and robust intelligent detection platform. Fusing several sensor signals increases the effectiveness of detection. Further, sensors will be influenced (i.e. detection parameters adapted) by events detected by other sensors in the vicinity. By integrating data, such as vehicle traffic, from outside the immediate border area pre-events can be detected and learned. The events will be analysed with machine learning tools to continuously increase the systems detection and tracking capability. FOLDOUT will make the tasks of Border Guards simpler and faster by combining events from various sensors to give a complete situation threat assessment combined with suggested reaction scenarios. A two year pilot in Bulgaria and demonstrators in Greece, Finland and French Guiana FOLDOUT will provide fundamental enhancements in the domain of border surveillance and improved search & rescue scenarios.