{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:19:02Z","timestamp":1775470742102,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T00:00:00Z","timestamp":1586995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["0001"],"award-info":[{"award-number":["0001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Amazon Fund\/Brazilian Development Bank (BNDES)\/FUNCATE","award":["17.2.0536.1"],"award-info":[{"award-number":["17.2.0536.1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2014Google 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.<\/jats:p>","DOI":"10.3390\/rs12081253","type":"journal-article","created":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T13:01:39Z","timestamp":1587042099000},"page":"1253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":255,"title":["An Overview of Platforms for Big Earth Observation Data Management and Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3239-2160","authenticated-orcid":false,"given":"Vitor","family":"Gomes","sequence":"first","affiliation":[{"name":"C4ISR Division, Institute for Advanced Studies (IEAv), S\u00e3o Jos\u00e9 dos Campos, SP 12228-001, Brazil"},{"name":"Image Processing Division, National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos, SP 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7534-0219","authenticated-orcid":false,"given":"Gilberto","family":"Queiroz","sequence":"additional","affiliation":[{"name":"Image Processing Division, National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos, SP 12227-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2656-5504","authenticated-orcid":false,"given":"Karine","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Image Processing Division, National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos, SP 12227-010, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2018.01.014","article-title":"Open and scalable analytics of large Earth observation datasets: From scenes to multidimensional arrays using SciDB and GDAL","volume":"138","author":"Appel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2017.11.007","article-title":"A versatile data-intensive computing platform for information retrieval from big geospatial data","volume":"81","author":"Soille","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/j.1467-9671.2010.01205.x","article-title":"Moving code in spatial data infrastructures\u2013web service based deployment of geoprocessing algorithms","volume":"14","author":"Bernard","year":"2010","journal-title":"Trans. GIS"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Camara, G., Assis, L.F., Ribeiro, G., Ferreira, K.R., Llapa, E., and Vinhas, L. (2016, January 31). Big earth observation data analytics: Matching requirements to system architectures. Proceedings of the 5th ACM SIGSPATIAL International Workshop On Analytics for Big Geospatial Data, Burlingame, CA, USA.","DOI":"10.1145\/3006386.3006393"},{"key":"ref_6","unstructured":"Rajabifard, A., and Williamson, I.P. (April, January 29). Spatial data infrastructures: Concept, SDI hierarchy and future directions. Proceedings of the GEOMATICS\u201980 Conference, Proceedings Geomatics, Tehran, Iran."},{"key":"ref_7","unstructured":"OGC (2019, December 12). OGC Standards and Supporting Documents. Available online: http:\/\/www.opengeospatial.org\/standards\/."},{"key":"ref_8","unstructured":"M\u00fcller, M. (2016). Service-Oriented Geoprocessing in Spatial Data Infrastructures. [Ph.D. Thesis, Technische Universit\u00e4t Dresden]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4720","DOI":"10.1109\/JSTARS.2015.2494610","article-title":"An SDI approach for big data analytics: The case on sensor web event detection and geoprocessing workflow","volume":"8","author":"Yue","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MGRS.2016.2600528","article-title":"Recent activities in Earth data science [technical committees]","volume":"4","author":"Yue","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_11","unstructured":"Woodcock, R., Cecere, T., Mitchell, A., Killough, B., Dyke, G., Ross, J., Albani, M., Ward, S., and Labahn, S. (2019, June 13). CEOS Future Data Access and Analysis Architectures Study. Available online: http:\/\/ceos.org\/document_management\/Meetings\/Plenary\/30\/Documents\/5.2_Future-Data-Architectures-Interim-Report_v.1.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sedona, R., Cavallaro, G., Jitsev, J., Strube, A., Riedel, M., and Benediktsson, J.A. (2019). Remote sensing big data classification with high performance distributed deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11243056"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Merticariu, G., Misev, D., and Baumann, P. (2015). Towards a general array database benchmark: Measuring storage access. Big Data Benchmarking, Springer.","DOI":"10.1007\/978-3-319-49748-8_3"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., and Widmann, N. (1998, January 1\u20134). The multidimensional database system RasDaMan. Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, WA, USA.","DOI":"10.1145\/276304.276386"},{"key":"ref_15","first-page":"21","article-title":"SciDB DBMS Research at M.I.T","volume":"36","author":"Stonebraker","year":"2013","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"349","DOI":"10.14778\/3025111.3025117","article-title":"The TileDB Array Data Storage Manager","volume":"10","author":"Papadopoulos","year":"2016","journal-title":"Proc. VLDB Endow."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, Z., Fox, G., and Zhou, M. (2012, January 13\u201316). Investigation of data locality in MapReduce. Proceedings of the 12th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012, Ottawa, ON, Canada.","DOI":"10.1109\/CCGrid.2012.42"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghemawat, S., Gobioff, H., and Leung, S.T. (2003, January 21\u201324). The Google File System. Proceedings of the 19th ACM Symposium on Operating Systems Principles, Bolton Landing, NY USA.","DOI":"10.1145\/945445.945450"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shvachko, K., Kuang, H., Radia, S., and Chansler Yahoo, R. (2010, January 3\u20137). The Hadoop Distributed File System. Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), Lake Tahoe, NV, USA.","DOI":"10.1109\/MSST.2010.5496972"},{"key":"ref_20","first-page":"012039","article-title":"A Survey on Distributed File System Technology","volume":"608","author":"Blomer","year":"2014","journal-title":"J. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.peva.2015.12.002","article-title":"Data locality in MapReduce: A network perspective","volume":"96","author":"Wang","year":"2016","journal-title":"Perform. Eval."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"991","DOI":"10.14393\/rbcv69n5-44011","article-title":"Big data streaming for remote sensing time series analytics using MapReduce","volume":"69","author":"Assis","year":"2017","journal-title":"Rev. Bras. Cartogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.future.2018.07.054","article-title":"High-Performance Computing for Big Data Processing","volume":"88","author":"Wu","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10707-009-0087-2","article-title":"The OGC web coverage processing service (WCPS) standard","volume":"14","author":"Baumann","year":"2010","journal-title":"GeoInformatica"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/17538947.2014.1003106","article-title":"Big data analytics for earth sciences: The EarthServer approach","volume":"9","author":"Baumann","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_26","first-page":"166","article-title":"Web Services for Big Earth Observation Data","volume":"69","author":"Vinhas","year":"2016","journal-title":"Geoinfo 2016"},{"key":"ref_27","unstructured":"Amazon Web Services (2020, March 26). Open Data on AWS. Available online: https:\/\/aws.amazon.com\/opendata\/."},{"key":"ref_28","unstructured":"Copernicus (2020, March 26). DIAS|Copernicus. Available online: https:\/\/www.copernicus.eu\/en\/access-data\/dias\/."},{"key":"ref_29","unstructured":"CREODIAS (2020, March 26). What is CREODIAS?. Available online: https:\/\/creodias.eu\/."},{"key":"ref_30","unstructured":"(2020, March 26). Mundi Web Services. Available online: https:\/\/mundiwebservices.com\/."},{"key":"ref_31","unstructured":"(2020, March 26). ONDA. Available online: https:\/\/www.onda-dias.eu\/."},{"key":"ref_32","unstructured":"(2020, March 26). WEkEO. Available online: https:\/\/www.wekeo.eu\/."},{"key":"ref_33","unstructured":"(2020, March 26). Sobloo. Available online: https:\/\/sobloo.eu\/."},{"key":"ref_34","unstructured":"Sinergise (2020, January 10). Sentinel Hub by Sinergise. Available online: https:\/\/www.sentinel-hub.com\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","unstructured":"(2019, June 13). Open Data Cube. Available online: https:\/\/www.opendatacube.org\/."},{"key":"ref_37","unstructured":"FAO (2020, February 07). SEPAL Repository. Available online: https:\/\/github.com\/openforis\/sepal\/."},{"key":"ref_38","unstructured":"Pebesma, E., Wagner, W., Schramm, M., Von Beringe, A., Paulik, C., Neteler, M., Reiche, J., Verbesselt, J., Dries, J., and Goor, E. (2017). OpenEO\u2014A Common, Open Source Interface Between Earth Observation Data Infrastructures and Front-End Applications, Technische Universitaet Wien. Technical Report."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.future.2016.06.009","article-title":"pipsCloud: High performance cloud computing for remote sensing big data management and processing","volume":"78","author":"Wang","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Goldblatt, R., You, W., Hanson, G., and Khandelwal, A. (2016). Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine. Remote Sens., 8.","DOI":"10.3390\/rs8080634"},{"key":"ref_42","unstructured":"Google (2020, March 27). Google Earth Engine. Available online: https:\/\/earthengine.google.com\/."},{"key":"ref_43","unstructured":"Sentinel-Hub (2020, January 10). Sentinel-Hub Documentation. Available online: https:\/\/docs.sentinel-hub.com\/api\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2017.03.015","article-title":"The Australian Geoscience Data Cube\u2014Foundations and lessons learned","volume":"202","author":"Lewis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_45","unstructured":"Open Data Cube (2020, February 07). Open Data Cube Manual. Available online: https:\/\/datacube-core.readthedocs.io\/en\/latest\/."},{"key":"ref_46","unstructured":"Open Data Cube (2020, February 07). Open Data Cube Repository. Available online: https:\/\/github.com\/opendatacube\/."},{"key":"ref_47","unstructured":"Open Data Cube (2019, June 13). The \u201cRoad to 20\u201d International Data Cube Deployments. Available online: https:\/\/www.opendatacube.org\/road-to-20\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1080\/20964471.2017.1398903","article-title":"Building an Earth Observations Data Cube: Lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)","volume":"1","author":"Giuliani","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ariza-Porras, C., Bravo, G., Villamizar, M., Moreno, A., Castro, H., Galindo, G., Cabera, E., Valbuena, S., and Lozano, P. (2017). CDCol: A Geoscience Data Cube that Meets Colombian Needs, Springer International Publishing. Advances in Computing.","DOI":"10.1007\/978-3-319-66562-7_7"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/608\/1\/012009","article-title":"Latest evolution of EOS filesystem","volume":"608","author":"Adde","year":"2015","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_51","unstructured":"OpenEO (2020, January 10). OpenEO Documentation. Available online: https:\/\/openeo.org\/documentation\/0.4\/."},{"key":"ref_52","unstructured":"OpenEO (2020, January 10). OpenEO\u2014Concepts and API Reference. Available online: https:\/\/open-eo.github.io\/openeo-api\/arch\/index.html."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Killough, B. (August, January 28). The Impact of Analysis Ready Data in the Africa Regional Data Cube. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898321"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/20964471.2017.1404232","article-title":"A View-Based Model of Data-Cube to Support Big Earth Data Systems Interoperability","volume":"1","author":"Nativi","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Appel, M., and Pebesma, E. (2019). On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library. Data, 4.","DOI":"10.32614\/CRAN.package.gdalcubes"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:30:53Z","timestamp":1760362253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,16]]},"references-count":55,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081253"],"URL":"https:\/\/doi.org\/10.3390\/rs12081253","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,16]]}}}