The Impact of Analysis Ready Data in the Africa Regional Data Cube

Killough, Brian

doi:10.1109/igarss.2019.8898321

Cited by 25 publications

(12 citation statements)

References 8 publications

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“…Recently, ODC has been used by different projects to manage multidimensional data cubes created from remote sensing images for a specific country, such as the Australian Data Cube [44], Swiss Data Cube [48] and Africa Regional Data Cube [53]. In these projects, Analysis Ready Data (ARD) generated from Earth observation satellite images are modeled as multidimensional data cubes, especially for image time series analysis [54].…”

Section: Final Remarks and Discussionmentioning

confidence: 99%

An Overview of Platforms for Big Earth Observation Data Management and Analysis

2020

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In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.

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Section: Final Remarks and Discussionmentioning

confidence: 99%

An Overview of Platforms for Big Earth Observation Data Management and Analysis

2020

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“…The Open Data Cube (ODC) is a framework that provides tools for indexing, visualization, processing, and analysis of EO data. It is an open-source framework that is used as an infrastructure for the management of data cubes for a growing number of projects, such as the Australian Geoscience Data Cube [12], Swiss Data Cube [14], and Digital Earth Africa [18].…”

Section: Applicationsmentioning

confidence: 99%

“…Other similar projects that generate ARD and data cubes for specific countries, such as Swiss Data Cube (SDC) [14], Digital Earth Australia (DE Australia) [12], and Digital Earth Africa (DE Africa) [18], are using the ODC framework. These other projects follow a similar approach for making data available, analysis, and processing.…”

Section: Final Remarks and Future Directionsmentioning

confidence: 99%

“…Recent initiatives to create EO data cubes from remote sensing images for specific regions include the Australian Data Cube [12], the Swiss Data Cube [14], the Armenian Data Cube [16], the Catalan Data Cube [17], and the Africa Regional Data Cube [18]. Meanwhile, novel software platforms have been developed to access, prepare, index, and handle large amounts of remote sensing images and data cubes, such as the Open Data Cube (ODC) [12], e-Sensing [19], and the Google Earth Engine (GEE) [20].…”

Section: Introductionmentioning

confidence: 99%

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Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

et al. 2020

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Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

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“…This idea is now applied to e.g., Armenia (Asmaryan et al, 2019), Colombia (Ariza-Porras et al, 2017Bravo et al, 2017), Kenya (Killough, 2019), Switzerland (Giuliani et al, 2017), among other countries. Previous work has focused on the implementation and perspectives of these and related initiatives that vary from the software development to its establishment as national projects for supporting decision making (Giuliani et al, 2017;Killough, 2019). However, these initiatives are based on a latitudelongitude grid for data storage.…”

Section: Introductionmentioning

confidence: 99%

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

et al. 2021

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Tropical ecosystems experience particularly fast transformations largely as a consequence of land use and climate change. Consequences for ecosystem functioning and services are hard to predict and require analyzing multiple data sets simultaneously. Today, we are equipped with a wide range of spatio-temporal observation-based data streams that monitor the rapid transformations of tropical ecosystems in terms of state variables (e.g., biomass, leaf area, soil moisture) but also in terms of ecosystem processes (e.g., gross primary production, evapotranspiration, runoff). However, the underexplored joint potential of such data streams, combined with deficient access to data and processing, constrain our understanding of ecosystem functioning, despite the importance of tropical ecosystems in the regional-to-global carbon and water cycling. Our objectives are: 1. To facilitate access to regional “Analysis Ready Data Cubes” and enable efficient processing 2. To contribute to the understanding of ecosystem functioning and atmosphere-biosphere interactions. 3. To get a dynamic perspective of environmental conditions for biodiversity. To achieve our objectives, we developed a regional variant of an “Earth System Data Lab” (RegESDL) tailored to address the challenges of northern South America. The study region extensively covers natural ecosystems such as rainforest and savannas, and includes strong topographic gradients (0–6,500 masl). Currently, environmental threats such as deforestation and ecosystem degradation continue to increase. In this contribution, we show the value of the approach for characterizing ecosystem functioning through the efficient implementation of time series and dimensionality reduction analysis at pixel level. Specifically, we present an analysis of seasonality as it is manifested in multiple indicators of ecosystem primary production. We demonstrate that the RegESDL has the ability to underscore contrasting patterns of ecosystem seasonality and therefore has the potential to contribute to the characterization of ecosystem function. These results illustrate the potential of the RegESDL to explore complex land-surface processes and the need for further exploration. The paper concludes with some suggestions for developing future big-data infrastructures and its applications in the tropics.

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The Impact of Analysis Ready Data in the Africa Regional Data Cube

Cited by 25 publications

References 8 publications

An Overview of Platforms for Big Earth Observation Data Management and Analysis

An Overview of Platforms for Big Earth Observation Data Management and Analysis

Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

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