Project description
AI4EO to measure global food security
Measuring global food security is essential to design effective decision-making policies. There are several assessment measures, but they focus on nutrition and physical aspects. Also, the difficulty of obtaining relevant data leads to incomplete assessments. In this context, the ERC-funded EO4FoodSecurity project will extend the unique AI algorithms and the big EO data management features developed in the ERC-funded So2Sat project to measure the state of global food security at regional levels. The project will develop software using multimodal data derived from satellite imagery and auxiliary open data as a commercial, integrated service. EO4FoodSecurity will develop a comprehensive business case to assist in designing an exploitation strategy and an interactive big EO data analysis platform.
Objective
Characterizing the state of global food security is essential in devising and evaluating policies and programs for effective decision making. The concept of food security is multidimensional and dynamic and is often compounded by the challenge of obtaining relevant data. Moreover, finding appropriate indicators that specifically encompass the four dimensions of food security (including physical availability of food, economic and physical access to food, food utilization, and sustainability) as specified by UN FAO remains a challenging task. There exist variety of different measures for assessing the food security situation, but they merely focus on nutrition and physical aspects and thus provide incomplete assessments related to the problem.
In this PoC project, I aim to extend the unique AI algorithms and the big EO data management features developed in the ERC StG “So2Sat” to characterize the state of global food security on regional scales using multimodal data derived from satellite imagery and auxiliary open data, and offer our software as a commercial, integrated service. Within the PoC, a comprehensive business case that will assist us in designing an exploitation strategy will be developed. Achieving these objectives will augment the capability of our existing AI solution for land cover/land use mapping to infer the crucial aspects of food security and sustainability.
Our value proposition in EO4FoodSecurity is a set of professional solutions to extract relevant indicators for characterizing food security by retrieving them from big EO data and other open sources using AI. E.g. generating land use map and using it along with other information extraction modules of So2Sat (such as population density, road and building footprints) and other open data (e.g. meteorological, nutrition) to generate food security map at unprecedented finer spatial and temporal scales. We aim to support these solutions in an easy-to-use, interactive big EO data analysis platform.
Fields of science
- natural sciencescomputer and information sciencesdata science
- natural sciencescomputer and information sciencessoftware
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- natural sciencesearth and related environmental sciencessoil sciencesland-based treatment
- medical and health scienceshealth sciencesnutrition
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-AG-LS - HORIZON Lump Sum GrantHost institution
80333 Muenchen
Germany