Datacubes: A Technology Survey

Baumann, Peter; Mišev, Dimitar; Merticariu, Vlad; Huu, Bang Pham; Bell, Brennan

doi:10.1109/igarss.2018.8518920

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…The canonical data cube for EO data is typically a dense array [24], where for (nearly) every combination of, e.g., 2D/3D coordinates, time, and spectral band, a valid pixel value is available. In practice, EO data are collected along a satellite's orbit and form a spatial trajectory; an upscaling of the spatial and temporal dimensions may become necessary to create dense data cubes (coverages of analysis ready data) from this information.…”

Section: Data Representationmentioning

confidence: 99%

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

et al. 2021

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At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers’ data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way.

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Section: Data Representationmentioning

confidence: 99%

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

et al. 2021

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“…in Baumann et al. ( 2018a ), we focus on overall trends in the historic context. Figure 3 illustrates the timeline of the emergence of different EO portals (see Table 1 for details), some of which utilise the aforementioned technologies, and clearly shows the increase in the number of available portals and solutions over time.…”

Section: Overview Of Existing Systems For Big Earth Data Management Amentioning

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

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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.

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“…This means that they can store data between many computing nodes with high availability and perform multiple aggregated tasks of great complexity independently. There is an emerging logical model called data cube which is also designed to remove the burden from scientists of having to compile BEOD and offer "analysis ready data" (ARD) [45]. This term stands for providing Earth Observation data with a reduced learning curve in real time for data wrangling satellite imagery and the reuse of processing methods for analysis [46][47][48].…”

Section: A Spatial Data Infrastructure For Big Earth Observation Datamentioning

confidence: 99%

TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

Assis

Ferreira

Vinhas

et al. 2019

IJGI

112

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The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

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Datacubes: A Technology Survey

Cited by 10 publications

References 7 publications

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

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

TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

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