Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential to revolutionise various aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential in a number of high impact applications, ranging from security and energy to agriculture 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 has developed and enriches the European AI-on-demand platform with a number of application bootstrapping services and tools to accelerate uptake and innovation, whilst it provides integration over AI-on-Demand services and the Copernicus ecosystem, targeting the highly successful Data and Information Access Service (DIAS) Cloud platforms. More specifically, by employing procedures for onboarding and validating models and tools, and by utilising a host of meticulously reviewed and supervised open calls-enabled projects, and containerisation best-practices, AI4Copernicus deployed and made available several products on DIAS platforms. Moreover, these products and resources have been made available on the AI-on-Demand platform catalogue for discovery, use and further development. The AI4Copernicus framework is being used by a number of business-driven projects and SMEs spanning several application domains. This article provides an overview of the European AI and EO context as well as the AI4Copernicus technological framework and tools offered. Further, we present real world use-cases as well as a community-centred evaluation of our framework based on usage and feedback received from several projects.
With regard to the Sustainable Development Goals (SDGs), which address a range of social, economic and environmental challenges faced by the world today, the present work is targeting a specific indicator of relevance for security. A methodology to support the calculation of the SDG indicator 13.1.1 (Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population) is presented; while the indicator is currently based on data from national demographic agencies, the proposed method incorporates the use of Earth Observation (EO) data. The workflow has been tested in the Sahel region (Niger), one of the hot-spots for Climate Security concerns.
The impact of climate change on the security and well-being of citizens is increasingly recognized as a threat multiplier, with consequences that can have a local, regional and global impact. A major consequence of this new security paradigm is the so-called Climate Security, which refers to how climate change related events amplify existing risks in society, endangering the safety of citizens, key infrastructures, economies or ecosystems. Earth Observation (EO) has long been regarded as a critical resource for understanding Climate Security scenarios. Together with technologies such as Big Data and Artificial Intelligence (AI), as well as the emergence of the Digital Twins concept, these technologies are providing a key framework for a holistic advanced analysis. The present work aims to present the European Union Satellite Centre (SatCen) approach to better understand Climate Security issues, displaying its different activities in Research and Innovation (R&I) as well as an example of an operational use case, where the construction of a water canal in the Amu Darya river basin could have significant consequences on the access to water for the whole region, leading to possible security concerns.
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.
The impact of climate change on the security and well-being of citizens is increasingly recognized as a threat multiplier, with consequences that can have a local, regional and global impact. Earth Observation (EO) resources have long been regarded as valuable tools for understanding climate security scenarios. The emergence of technologies such as Big Data and Artificial Intelligence (AI) has provided the infrastructure and tools needed for advanced analysis. This paper demonstrates how the integration of ancillary data (e.g. statistics, economics, meteorological) and heterogeneous data could support a better understanding of climate security scenarios. The study utilizes three years of Sentinel-2 data to generate maps of areas that are more susceptible to potential flooding. Normal and anomalous situations were analyzed, exploring the temporal distribution of variables to characterize seasonal cycles in a climate security hotspot: the Sahel region.
