GEOSS Platform data content and use

The GEOSS Platform is a key contribution to the goal of building the Global Earth Observation System of Systems(GEOSS).It enables a harmonized discovery and access of Earth observation data,shared online by heterogeneous organizations worldwide.This work analyzes both what is made available in the GEOSS Platform by the data providers and how users are utilizing it including multiyear trends,updating a previous analysis published in 2017.The present statistics derive from a 2021 EOValue report funded by the European Commission.The offer of GEOSS Platform data has been the object of various analyses,including data provider characterization,data sharing trends,and data characterization(comprising metadata quality analysis,thematic analysis,responsible party identification,spatial–temporal coverage).GEOSS data demand has also been the object of several analyses,including data consumer characterization,utilization trends,and requested data characterization(comprising thematic analysis,spatial–temporal coverage,and popularity).Among thefindings,a large amount of shared data,mostly from satellite sources,emerges with an issue of low metadata quality and related discovery match.Moreover,the trend in usage is decreasing.Therefore,the progressive disconnection of the GEOSS platform from its data Providers and Users and other possible causes are also reported.

Big Earth Data science:an information framework for a sustainable planet

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 decisionmaking 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.

Digital Earth 2020:towards the vision for the next decade

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.

A view-based model of data-cube to support big earth data systems interoperability

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.

Exploring the depths of the global earth observation system of systems

This paper explores for the first time the contents,structure and relationships across institutions and disciplines of a global Big Earth Data cyber-infrastructure:the Global Earth Observation System of System(GEOSS).The analysis builds on 1.8 million metadata records harvested in GEOSS.Because this set includes almost all the major large data collections in GEOSS,the analysis represents more than 80%of all the data made available through this global system.We explore two major aspects:the collaborative networks and the thematic coverage in GEOSS.The first connects the contributing organisations through the more than 200,000 keywords used in the systems,and then explores who is citing whom,a proxy for of institutional thickness.The thematic coverage is analysed through neural network algorithms,first on the keywords,and then on the corpus of 653 million lemmatised lower case words built from the titles and abstracts of all 1.8 million metadata records.The findings not only give a good overview of the GEOSS data universe,but offer immediate priorities on how to increase the usability of GEOSS through improved data management,and the opportunity to augment the metadata with high level concept that synthetise well the contents of the data-set.

Data democracy–increased supply of geospatial information and expanded participatory processes in the production of data

The global landscape in the supply,co-creation and use of geospatial data is changing very rapidly with new satellites,sensors and mobile devices reconfiguring the traditional lines of demand and supply and the number of actors involved.In this paper we chart some of these technology-led developments and then focus on the opportunities they have created for the increased participation of the public in generating and contributing information for a wide range of uses,scientific and non.Not all this information is open or geospatial,but sufficiently large portions of it are to make it one of the most significant phenomena of the last decade.In fact,we argue that while satellite and sensors have exponentially increased the volumes of geospatial information available,the participation of the public is transformative because it expands the range of participants and stakeholders in society using and producing geospatial information,with opportunities for more direct participation in science,politics and social action.