This position paper is the outcome of a brainstorming workshop organised by the International Society for Digital Earth (ISDE) in Beijing in March 2011. It argues that the vision of Digital Earth (DE) put forward by Vice-President Al Gore 13 years ago needs to be re-evaluated in the light of the many developments in the fields of information technology, data infrastructures and earth observation that have taken place since. The paper identifies the main policy, scientific and societal drivers for the development of DE and illustrates the multi-faceted nature of a new vision of DE grounding it with a few examples of potential applications. Because no single organisation can on its own develop all the aspects of DE, it is essential to develop a series of collaborations at the global level to turn the vision outlined in this paper into reality.

This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.

The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before. These changes will unlikely stop or even decelerate in the near future. There is an urgent need for a new scientific approach and an advanced form of evidence-based decision-making towards the benefit of society, the economy, and the environment. To understand the impacts and interrelationships between humans as a society and natural Earth system processes, we propose a new engineering discipline, Big Earth Data science. This science is called to provide the methodologies and tools to generate knowledge from diverse, numerous, and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth. Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves. The manuscript introduces the universe of discourse characterizing this new science, its foundational paradigms and methodologies, and a possible technological framework to be implemented by applying an ecosystem approach. CASEarth and GEOSS are presented as examples of international implementation attempts. Conclusions discuss important challenges and collaboration opportunities.

The vision of Digital Earth (DE) put recently forward under the auspices of the International Society for DE extends the paradigm of spatial data infrastructures by advocating an interactive and dynamic framework based on near-to-real time information from sensors and citizens. This paper contributes to developing that vision and reports the results of a two-year research project exploring the extent to which it is possible to extract information useful for policy and science from the large volumes of messages and photos being posted daily through social networks. Given the noted concerns about the quality of such data in relation to that provided by authoritative sources, the research has developed a semi-automatic workflow to assess the fitness for purpose of data extracted from Twitter and Flickr, and compared them to that coming from official sources, using forest fires as a case study. The findings indicate that we were able to detect accurately six of eight major fires in France in the summer of 2011, with another four detected by the social networks but not reported by our official source, the European Forest Fire Information Service. These findings and the lessons learned in handling the very large volumes of unstructured data in multiple languages discussed in this study provide useful insights into the value of social network data for policy and science, and contribute to advancing the vision of DE.

Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data, especially for managing satellite time series. These infrastructures build on the concept of multidimensional data model (data hypercube) and are complex systems engaging different disciplines and expertise. For this reason, their interoperability capacity has become a challenge in the Global Change and Earth System science domains. To address this challenge, there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features. In this respect, a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain. This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas, with the final goal of enabling and facilitating interoperability. It introduces six modeling views, each of them is described according to: its main concerns, principal stakeholders, and possible patterns to be used. The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional “cubes” along with the more recent and analogous development in the Earth Observation domain, and puts forward a set of interoperability recommendations based on the modeling views.

The widespread diffusion of sensors, mobile devices, social media and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. This in turn is creating both opportunities and challenges for policy-making and science. There can be major benefits from the deployment of the IoT in smart cities and environmental monitoring, but to realize such benefits, and reduce potential risks, there is an urgent need to address current limitations, including the interoperability of sensors, data quality, security of access and new methods for spatio-temporal analysis. Within this context, the manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels. AirSensEUR is described from the perspective of interoperable data management with emphasis on possible use case scenarios, where reliable and timely air quality data would be essential.