Description du projet
AI4EO pour mesurer la sécurité alimentaire mondiale
Il est essentiel de mesurer la sécurité alimentaire mondiale à l’heure d’élaborer des politiques efficaces en matière de prise de décisions. Plusieurs mesures d’évaluation existent, mais elles portent toutes sur la nutrition et les aspects physiques. Qui plus est, la difficulté d’obtenir des données pertinentes se traduit par des évaluations incomplètes. Dans ce contexte, le projet EO4FoodSecurity, financé par le CER, étendra les capacités des algorithmes d’IA uniques et les fonctionnalités de gestion des mégadonnées d’OT développées dans le cadre du projet So2Sat, lui aussi financé par le CER, afin de mesurer l’état de la sécurité alimentaire mondiale au niveau régional. Le projet mettra au point un logiciel utilisant des données multimodales provenant d’images satellitaires et des données ouvertes auxiliaires en tant que service commercial intégré. EO4FoodSecurity préparera un dossier commercial complet pour contribuer à l’élaboration d’une stratégie d’exploitation et une plateforme interactive d’analyse des mégadonnées d’OT.
Objectif
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.
Champ scientifique
- 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
Régime de financement
HORIZON-AG-LS - HORIZON Lump Sum GrantInstitution d’accueil
80333 Muenchen
Allemagne