Descripción del proyecto
Avanzar en la integración de los datos satelitales para mejorar los resultados de la observación de la Tierra
A medida que la inteligencia artificial se extiende, aumenta la necesidad de ofrecer productos y servicios de observación de la Tierra con valor añadido. El proyecto financiado con fondos europeos CALLISTO prevé integrar los datos de Copernicus, ya indexados en plataformas DIAS (siglas inglesas de servicios de acceso a los datos y la información) como ONDA-DIAS, utilizando infraestructuras informáticas de alto rendimiento para mejorar la escalabilidad cuando sea necesario. Las fuentes de datos distribuidos complementarias incluyen los datos de posicionamiento de Galileo y el contenido visual de los vehículos aéreos no tripulados, así como los datos de la web y de las redes sociales, vinculados con datos geoespaciales abiertos y de sensores «in situ». A continuación, se aplican métodos de inteligencia artificial para extraer conocimientos vitales para los usuarios finales.
Objetivo
Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. Large volumes of satellite data (images and associated metadata) are frequently coming to the Earth from Sentinel constellation, offering a basis for creating value-added products that go beyond the space sector. The analysis and data fusion of all streams of data need to take advantage of the existing DIAS and HPC infrastructures, as well as the Galileo-enabled mobile devices when required by the involved end users to deliver fully automated processes in decision support systems. CALLISTO project integrates Copernicus data, already indexed in DIAS platforms such as ONDA-DIAS, utilising High Performance Computing infrastructures for enhanced scalability when needed. Complementary distributed data sources involve Galileo positioning data, visual content from UAVs, Web and social media data linking them with open geospatial data, in-situ sensor data. On top of these data sources, AI methods are applied to extract meaningful knowledge such as concepts, changes, activities, events, 3D-models, videos and animations of the user community. AI methods are also executed at the edge, offering enhanced scalability and timely services. The analysis of the extracted knowledge is performed in a semantic way and the associated analytics are delivered to the end users in non-traditional interfaces, including Augmented Reality, Virtual Reality and Mixer Reality in general. Data fusion among several types of data sources is provided on-demand, based on the end user requirements. The AI methods are trained to offer new virtual and augmented reality applications to water utility operators, journalists for the media sector, EU agriculture and CAP policymakers, and security agencies.
Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligence
- social sciencesmedia and communicationsjournalism
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencessoftwaresoftware applicationsvirtual reality
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SPACE-2020
Régimen de financiación
RIA - Research and Innovation actionCoordinador
00144 Roma
Italia