Overview of the Open Data Cube Initiative

Killough, Brian

doi:10.1109/igarss.2018.8517694

Cited by 80 publications

(48 citation statements)

References 3 publications

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“…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

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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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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

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“…Currently, the SDC contains 35 years of analysis-ready data of both optical (e.g., Landsat 5, 7, 8; Sentinel-2) and radar (e.g., Sentinel-1) satellite data over the entire country (total volume: 6TB; 200 billion observations) [30]. The SDC is an innovative analytical framework, based on the Open Data Cube software stack [14,31], allowing users to benefit from this new generation of EO data, and in particular minimizing the time and scientific knowledge required to access, prepare, and make it possible to analyze a large volume of data with consistent and spatially-aligned, calibrated observations. The SDC is aiming at contributing to the national Digital Switzerland strategy by (1) supporting innovation and growth, (2) improving efficiency and effectiveness of government investments, (3) improving management of natural resources, (4) stimulating research, (5) generating decision-ready information products, and (6) improving data access and use and enabling new products/services that can transform everyday life.…”

Section: Overview Of the Swiss Data Cubementioning

confidence: 99%

National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

Dhu

Giuliani

Juárez

et al. 2019

Data

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The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.

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“…The main advantage of ingesting data is that the data can be stored in a query-optimised way, and specific access patterns can be realised more efficiently, such as time series analysis or spatial analysis.Various technical solutions to create these logical views on EO data have rapidly gained traction over the past few years. The first national scale EO data cube was established in Australia [5], whose technology is now the basis of Digital Earth Australia [6] and the Open Data Cube (ODC) [7].The free and open source ODC technology is also behind other operational EO data cubes, such as in Switzerland [8], Colombia [9], Vietnam [10], the Africa Regional Data Cube [11] and at least nine other national or regional initiatives under development [7]. Rasdaman [12], an array database system that has been around since the mid-1990s, is another leading technology behind initiatives such as EarthServer [13] and the Copernicus Data and Exploitation platform for Germany [14]).…”

mentioning

confidence: 99%

“…Various technical solutions to create these logical views on EO data have rapidly gained traction over the past few years. The first national scale EO data cube was established in Australia [5], whose technology is now the basis of Digital Earth Australia [6] and the Open Data Cube (ODC) [7].…”

mentioning

confidence: 99%

“…The free and open source ODC technology is also behind other operational EO data cubes, such as in Switzerland [8], Colombia [9], Vietnam [10], the Africa Regional Data Cube [11] and at least nine other national or regional initiatives under development [7]. Rasdaman [12], an array database system that has been around since the mid-1990s, is another leading technology behind initiatives such as EarthServer [13] and the Copernicus Data and Exploitation platform for Germany [14]).…”

mentioning

confidence: 99%

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Semantic Earth Observation Data Cubes

Augustin

Sudmanns

Tiede

et al. 2019

Data

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There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.names. The main advantage of ingesting data is that the data can be stored in a query-optimised way, and specific access patterns can be realised more efficiently, such as time series analysis or spatial analysis.Various technical solutions to create these logical views on EO data have rapidly gained traction over the past few years. The first national scale EO data cube was established in Australia [5], whose technology is now the basis of Digital Earth Australia [6] and the Open Data Cube (ODC) [7].The free and open source ODC technology is also behind other operational EO data cubes, such as in Switzerland [8], Colombia [9], Vietnam [10], the Africa Regional Data Cube [11] and at least nine other national or regional initiatives under development [7]. Rasdaman [12], an array database system that has been around since the mid-1990s, is another leading technology behind initiatives such as EarthServer [13] and the Copernicus Data and Exploitation platform for Germany [14]). Other software implementations exist, such as the Earth System Data Cube from the European Space Agency [15] and SciDB [16].State-of-the-art EO data cubes simplify data provision to users by facilitating data uptake and aiming to provide analysis-ready data (ARD) [4]. While there is still an ongoing discussion about how ARD are defined and specified, it is usually understood as calibrated data, and in the case of CARD4L (Committee on EO Satellites ARD for Land), even contains masks as a target requirement specification, such as for cloud and water [17,18]. The intention is to shift the burden of pre-processing from users to data providers, who are often better equipped to consistently...

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Overview of the Open Data Cube Initiative

Cited by 80 publications

References 3 publications

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

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

National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

Semantic Earth Observation Data Cubes

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