Descripción del proyecto
Inteligencia artificial en datos de observación de la Tierra para medir la seguridad alimentaria mundial
Medir la seguridad alimentaria mundial es fundamental para diseñar políticas eficaces de toma de decisiones. Aunque existen diferentes medidas de evaluación, estas se centran en aspectos nutricionales y físicos. Además, la dificultad de obtener datos pertinentes da lugar a evaluaciones incompletas. En este contexto, el proyecto EO4FoodSecurity, financiado por el Consejo Europeo de Investigación, ampliará los algoritmos únicos de inteligencia artificial y las características de gestión de datos masivos de observación de la Tierra (OT o EO por sus siglas en inglés) desarrolladas en el proyecto financiado por el CEI So2Sat, para medir el estado de la seguridad alimentaria mundial a nivel regional. El equipo del proyecto desarrollará un «software» que utiliza datos multimodales procedentes de imágenes satelitales y datos abiertos auxiliares como un servicio comercial integrado. En EO4FoodSecurity se elaborará un caso de negocio integral para ayudar a diseñar una estrategia de explotación y una plataforma interactiva de análisis de datos masivos de OT.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-AG-LS - HORIZON Lump Sum GrantInstitución de acogida
80333 Muenchen
Alemania