Fostering Cross-Disciplinary Earth Science Through Datacube Analytics

Baumann, Peter; Rossi, A. P.; Bell, Brennan; Clements, Oliver; Evans, Ben; Hoenig, Heike; Hogan, Patrick; Kakaletris, George; Koltsida, Panagiota; Mantovani, Simone; Figuera, Ramiro Marco; Merticariu, Vlad; Mišev, Dimitar; Pham, Huu Bang; Siemen, Stephan; Wagemann, Julia

doi:10.1007/978-3-319-65633-5_5

Cited by 29 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is due to several reasons: (1) increasing volumes of data generated by EO satellites; (2) lack of expertise, infrastructure, or internet bandwidth to efficiently and effectively access, process, and utilize EO data; (3) the particular type of highly structured data that EO data represent introducing challenges when trying to integrate or analyze them; (4) and the substantial effort and cost required to store and process data limits the efficient use of these data (CEOS, 2017;Lewis et al, 2016;Purss et al, 2015). Therefore, EO data can be considered as Big Data, data that are too large, fast-lived, heterogeneous, or complex to get understood and exploited (Baumann, Rossi, et al, 2016). Consequently, we need new approaches to fully benefit from EO data and (1) unlock the information power of EO data; (2) broaden the use of EO data to a wider range of communities; and (3) support decisions-makers with the knowledge they require by systematically analyzing all available observations and convert them into meaningful geophysical variables.…”

Section: Introductionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

View full text Add to dashboard Cite

“…In recent years, the term EO data cube (or geospatial data cube, sometimes data cube only) has emerged to describe a new solution to store, organise, manage and analyse EO data (Baumann et al 2016(Baumann et al , 2018bGiuliani et al 2017;Purss et al 2015). EO data cube technology is tightly linked to the aforementioned term ARD (Baumann 2017a(Baumann , 2017bGiuliani et al 2017;Lewis et al 2016Lewis et al , 2017.…”

Section: Processing and Analysingmentioning

confidence: 99%

Big Earth data: disruptive changes in Earth observation data management and analysis?

Sudmanns

Tiede

Lang

et al. 2019

International Journal of Digital Earth

142

View full text Add to dashboard Cite

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term 'big Earth data' emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

show abstract

“…It is now possible to benefit from the computing power of these infrastructures while using interoperable processing services in a transparent manner, hiding the complexity of these infrastructure to users [68][69][70]. Such integration can help leveraging the capabilities of these infrastructures and support model-as-a-Service approaches, such as the GEO Model Web [71] or data cubes [18].…”

Section: Data Processingmentioning

confidence: 99%

Paving the Way to Increased Interoperability of Earth Observations Data Cubes

Giuliani

Masó

Mazzetti

et al. 2019

Data

View full text Add to dashboard Cite

Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users to harness big EO data at a minimum cost and effort. This significant interest is illustrated by various implementations that exist. The novelty of the approach results in different innovative solutions and the lack of commonly agreed definition of EODC. Consequently, their interoperability has been recognized as a major challenge for the global change and Earth system science domains. The objective of this paper is preventing EODC from becoming silos of information; to present how interoperability can be enabled using widely-adopted geospatial standards; and to contribute to the debate of enhanced interoperability of EODC. We demonstrate how standards can be used, profiled and enriched to pave the way to increased interoperability of EODC and can help delivering and leveraging the power of EO data building, efficient discovery, access and processing services.

show abstract

Fostering Cross-Disciplinary Earth Science Through Datacube Analytics

Cited by 29 publications

References 15 publications

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Big Earth data: disruptive changes in Earth observation data management and analysis?

Paving the Way to Increased Interoperability of Earth Observations Data Cubes

Contact Info

Product

Resources

About