Slide 1

Produção, visualização e análise de grandes volumes de imagens de sensoriamento remoto modeladas como cubos de dados multidimensionais para todo o território brasileiro.

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

by Vitor C. F. Gomes 1,2, Gilberto R. Queiroz 2 and Karine R. Ferreira 2

1C4ISR Division, Institute for Advanced Studies (IEAv), São José dos Campos, SP 12228-001, Brazil

2Image Processing Division, National Institute for Space Research (INPE), São José dos Campos, SP 12227-010, Brazil

Remote Sens. 202012(8), 1253;

Publisher: mdpi | Published: 16 April 2020

(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)


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. View Full-Text

Keywords: big Earth observation dataGoogle Earth EngineSentinel HubOpen Data CubeSEPALJEODPPpipsCloud

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

Gomes, V.C.F.; Queiroz, G.R.; Ferreira, K.R. An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens. 2020, 12, 1253.

Brazil Data Cube - 2019 - 2022