Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential of transforming virtually all aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential for a number of applications, covering high-impact domains as diverse as security, agriculture, energy and health. In this paper, we present the AI4Copernicus framework for bridging the two domains within the European context to enable data-centred innovation. In order to achieve this goal, AI4Copernicus enriches the European AI-on-demand platform with a number of bootstrapping services and tools to accelerate uptake and innovation, whilst it provides integration over AI-on-Demand services and the Copernicus ecosystem over the highly successful Data and Information Access Services (DIAS) Cloud platforms. The AI4Copernicus framework is being used by a number of business-driven projects spanning several application domains. In this paper, we provide an overview of the European AI and EO approach as well as of the AI4Copernicus technological framework and tools offered. Further, we describe exemplary real world use-cases as well as technological evaluation of our framework based on usage and feedback received from a number of projects.

Bridging the European Earth-Observation and AI Communities for Data-Intensive Innovation / Troumpoukis, Antonis; Klampanos, Iraklis; Pantazi, Despina-Athanasia; Tsalapati, Eleni; Albughdadi, Mohanad; Alexe, Mihai; Baousis, Vasileios; Barrilero, Omar; Billière, Bryce; Bojor, Alexandra; Branco, Pedro; Bruzzone, Lorenzo; Chietera, Andreina; Fournand, Philippe; Hall, Richard; Hassan, David; Lazzarini, Michele; Luna, Adrian; Punjani, Dharmen; Stamoulis, George; Weikmann, Giulio; Ziółkowski, Marcin; Ziouvelou, Xenia; Koubarakis, Manolis; Karkaletsis, Vangelis. - (2023), pp. 9-16. (Intervento presentato al convegno BigDataService 2023 tenutosi a Athens, Greece nel 17th-20th July 2022) [10.1109/BigDataService58306.2023.00008].

Bridging the European Earth-Observation and AI Communities for Data-Intensive Innovation

Bruzzone, Lorenzo;Weikmann, Giulio;
2023-01-01

Abstract

Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential of transforming virtually all aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential for a number of applications, covering high-impact domains as diverse as security, agriculture, energy and health. In this paper, we present the AI4Copernicus framework for bridging the two domains within the European context to enable data-centred innovation. In order to achieve this goal, AI4Copernicus enriches the European AI-on-demand platform with a number of bootstrapping services and tools to accelerate uptake and innovation, whilst it provides integration over AI-on-Demand services and the Copernicus ecosystem over the highly successful Data and Information Access Services (DIAS) Cloud platforms. The AI4Copernicus framework is being used by a number of business-driven projects spanning several application domains. In this paper, we provide an overview of the European AI and EO approach as well as of the AI4Copernicus technological framework and tools offered. Further, we describe exemplary real world use-cases as well as technological evaluation of our framework based on usage and feedback received from a number of projects.
2023
IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService 2023)
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
IEEE
979-8-3503-3379-4
Troumpoukis, Antonis; Klampanos, Iraklis; Pantazi, Despina-Athanasia; Tsalapati, Eleni; Albughdadi, Mohanad; Alexe, Mihai; Baousis, Vasileios; Barrile...espandi
Bridging the European Earth-Observation and AI Communities for Data-Intensive Innovation / Troumpoukis, Antonis; Klampanos, Iraklis; Pantazi, Despina-Athanasia; Tsalapati, Eleni; Albughdadi, Mohanad; Alexe, Mihai; Baousis, Vasileios; Barrilero, Omar; Billière, Bryce; Bojor, Alexandra; Branco, Pedro; Bruzzone, Lorenzo; Chietera, Andreina; Fournand, Philippe; Hall, Richard; Hassan, David; Lazzarini, Michele; Luna, Adrian; Punjani, Dharmen; Stamoulis, George; Weikmann, Giulio; Ziółkowski, Marcin; Ziouvelou, Xenia; Koubarakis, Manolis; Karkaletsis, Vangelis. - (2023), pp. 9-16. (Intervento presentato al convegno BigDataService 2023 tenutosi a Athens, Greece nel 17th-20th July 2022) [10.1109/BigDataService58306.2023.00008].
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