Projektbeschreibung
Fortschritte bei der Integration von Satellitendaten für bessere Ergebnisse von Erdbeobachtungen
Da die künstliche Intelligenz stetige Fortschritte macht, wird es zunehmend wichtiger, Erdbeobachtungsprodukte und -dienstleistungen mit Mehrwert bereitzustellen. Das EU-finanzierte Projekt CALLISTO plant, die Copernicus-Daten einzubeziehen, die bereits in DIAS-Plattformen wie ONDA-DIAS indiziert sind, und dabei wenn nötig Infrastrukturen für Hochleistungsrechner einzusetzen, wenn eine verbesserte Skalierbarkeit erreicht werden soll. Zu den ergänzenden verteilten Datenquellen gehören Positionsdaten von Galileo, Bildmaterial von unbesetzten Luftfahrzeugen sowie Daten aus dem Internet und den sozialen Medien, die mit offenen Raum- und In-situ-Sensordaten verbunden sind. Mithilfe von Verfahren der künstlichen Intelligenz werden dann wichtige Erkenntnisse für Endnutzende gewonnen.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-SPACE-2020
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
00144 Roma
Italien