Big earth observation data analytics

Câmara, Gilberto; Assis, Luiz Fernando Ferreira Gomes de; Ribeiro, Gilberto Q.; Ferreira, Karine Reis; Llapa, Eduardo; Vinhas, Lúbia

doi:10.1145/3006386.3006393

Cited by 43 publications

(9 citation statements)

References 27 publications

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“…These technologies should be integrated and presented to scientists in the form of a platform where the complexities of data storage, processing, and infrastructure must be abstracted. According to Camara et al [5], an architecture for big EO data analysis must meet the following requirements:…”

Section: Assessment Of the Platformsmentioning

confidence: 99%

“…Only in 2019, the volume of open data produced by Landsat-7 and Landsat-8, MODIS (Terra and Aqua units), and the three first Sentinel missions (Sentinel-1, -2 and -3) is around 5 PB [3]. These big data sets often exceed the memory, storage and processing capacities of personal computers; imposing severe limits that lead users to take advantage of only a small portion of the available data for scientific research and operational applications [2,4,5]. Thus, there is a need for novel technological solutions to properly store, process, disseminate and analyze these big EO data sets.…”

Section: Introductionmentioning

confidence: 99%

“…Data ownership: historical out-of-the-box data series should be available without reliance on external services. The demands and needs presented by Camara et al [5] and Ariza-Porras et al [49] motivated us to define ten capabilities to serve as criteria for evaluating the platforms presented in this work. The following list provides each capability name, its description, and, when applicable, its associated term proposed by other authors in parentheses:…”

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confidence: 99%

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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: Assessment Of the Platformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

2020

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“…SciDB's platform uses arrays as the primary data structure and has been co-opted by the geospatial community. 9,17,26,27 SciDB's massively parallel processing (shared-nothing parallel database) architecture allows it to process multi-dimensional arrays or geospatial imagery that are multiple petabytes. 10,25 SciDB is not the only array database platform.…”

Section: Platform Descriptionsmentioning

confidence: 99%

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Haynes¹,

Mitchell²,

Shook³

2020

Preprint

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Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

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“…Recently, public space agencies opened their archives, providing access to large Earth observation data sets. To use these data sets, researchers are building new data analysis methods (Câmara et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal calculus for reasoning about land-use trajectories

Maciel

Câmara

Vinhas

et al. 2018

International Journal of Geographical Information Science

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Earth observation images are a powerful source of data about changes in our planet. Given the magnitude of global environmental changes taking place, it is important that Earth Science researchers have access to spatiotemporal reasoning tools. One area of particular interest is land-use change. Using data obtained from images, researchers would like to express abstractions such as 'land abandonment', 'forest regrowth', and 'agricultural intensification'. These abstractions are specific types of land-use trajectories, defined as multi-year paths from one land cover into another. Given this need, this paper introduces a spatiotemporal calculus for reasoning about land-use trajectories. Using Allen's interval logic as a basis, we introduce new predicates that express cases of recurrence, conversion and evolution in land-use change. The proposed predicates are sufficient and necessary to express different kinds of land-use trajectories. Users can build expressions that describe how humans modify Earth's terrestrial surface. In this way, scientists can better understand the environmental and economic effects of land-use change. ARTICLE HISTORY

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Big earth observation data analytics

Cited by 43 publications

References 27 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

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

A spatiotemporal calculus for reasoning about land-use trajectories

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