{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:30:16Z","timestamp":1780554616111,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004421","name":"World Bank Group","doi-asserted-by":"publisher","award":["P143185"],"award-info":[{"award-number":["P143185"]}],"id":[{"id":"10.13039\/100004421","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ijgi8110513","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T09:11:27Z","timestamp":1573636287000},"page":"513","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":129,"title":["TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6094-6530","authenticated-orcid":false,"given":"Luiz Fernando","family":"F. G. 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(2014). Satellite-Based Forest Clearing Detection in the Brazilian Amazon: FORMA, DETER, and PRODES, World Resources Institute."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1590\/1809-4392201505504","article-title":"High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5\/TM and MODIS data","volume":"46","author":"Almeida","year":"2016","journal-title":"Acta Amaz."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1126\/science.307.5712.1043c","article-title":"Amazonian deforestation models","volume":"307","author":"Aguiar","year":"2005","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Assis, L.F.F.D., EA Horita, F., P de Freitas, E., Ueyama, J., and de Albuquerque, J.P. (2018). A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management. Sensors, 18.","DOI":"10.3390\/s18061689"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Namikawa, L.M., K\u00f6rting, T.S., and Castejon, E.F. (2016). Water body extraction from Rapideye images: An automated methodology based on hue component of color transformation from RGB to HSV model. Revista Brasileira de Cartografia, 68.","DOI":"10.14393\/rbcv68n6-44495"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.isprsjprs.2018.08.007","article-title":"Big earth observation time series analysis for monitoring Brazilian agriculture","volume":"145","author":"Picoli","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1080\/13658816.2018.1520235","article-title":"A spatiotemporal calculus for reasoning about land-use trajectories","volume":"33","author":"Maciel","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","first-page":"272","article-title":"Monitoring tropical forest from space: The PRODES digital project","volume":"35","author":"Valeriano","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","first-page":"2013","article-title":"Monitoramento da floresta Amaz\u00f4nica Brasileira por sat\u00e9lite","volume":"25","author":"Prodes","year":"2013","journal-title":"Inst. Nac. De Pesqui. Espac. Proj. Prodes."},{"key":"ref_10","first-page":"2934","article-title":"Avalia\u00e7\u00e3o de dados dos Sistemas de Alerta da Amaz\u00f4nia: DETER e SAD","volume":"15","author":"Escada","year":"2011","journal-title":"Simp\u00f3sio Bras. De Sensoriamento Remoto"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1109\/JSTARS.2015.2437075","article-title":"DETER-B: The new Amazon near real-time deforestation detection system","volume":"8","author":"Diniz","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1016\/j.rse.2008.05.012","article-title":"Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover","volume":"112","author":"Hansen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/00050351.1999.10558768","article-title":"Spatial data infrastructure concepts","volume":"44","author":"Phillips","year":"1999","journal-title":"Aust. Surv."},{"key":"ref_14","unstructured":"Bernard, L., and Craglia, M. (April, January 31). SDI-from spatial data infrastructure to service driven infrastructure. Proceedings of the Research Workshop on Cross-Learning between Spatial Data Infrastructures and Information Infrastructures, Enschede, The Netherlands."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0198-9715(04)00049-3","article-title":"The European geoportal\u2014One step towards the establishment of a European Spatial Data Infrastructure","volume":"29","author":"Bernard","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1080\/13658810110096001","article-title":"Developing a common spatial data infrastructure between State and Local Government\u2014An Australian case study","volume":"16","author":"Jacoby","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0198-9715(04)00045-6","article-title":"The emergence of geoportals and their role in spatial data infrastructures","volume":"29","author":"Maguire","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_18","unstructured":"Masser, I. (2005). GIS Worlds: Creating Spatial Data Infrastructures, ESRI Press."},{"key":"ref_19","first-page":"9","article-title":"Big Data\u2014A step change for SDI?","volume":"11","author":"Tsinaraki","year":"2016","journal-title":"Int. J."},{"key":"ref_20","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_21","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_22","unstructured":"Camara, G., Ribeiro, G., Vinhas, L., Ferreira, K.R., Cartaxo, R., Sim\u00f5es, R., Llapa, E., Assis, L.F., and Sanchez, A. (2017, January 28\u201330). The e-Sensing architecture for big Earth observation data analysis. Proceedings of the 2017 Conference on Big Data from Space (BiDS\u201917), Toulouse, France."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bordogna, G., Kliment, T., Frigerio, L., Brivio, P., Crema, A., Stroppiana, D., Boschetti, M., and Sterlacchini, S. (2016). A spatial data infrastructure integrating multisource heterogeneous geospatial data and time series: A study case in agriculture. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5050073"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1080\/17538947.2017.1351583","article-title":"Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data","volume":"11","author":"Wagemann","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10708-008-9189-x","article-title":"Reconceptualizing the role of the user of spatial data infrastructure","volume":"72","author":"Budhathoki","year":"2008","journal-title":"GeoJournal"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TKDE.2007.250587","article-title":"Resource discovery in a European spatial data infrastructure","volume":"19","author":"Smits","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","unstructured":"Chan, T.O., and Williamson, I. (1999, January 22\u201326). Spatial data infrastructure management: Lessons from corporate GIS development. Proceedings of the 27th Annual Conference of AURISA 99, Blue Mountains, Australia."},{"key":"ref_28","unstructured":"Assis, L.F.F.G., Ferreira, K.R., Vinhas, L., Maurano, L., Almeida, C.A., Nascimento, J.R., Carvalho, A.F.A., Camargo, C., and Maciel, A.M. (2019, January 14\u201317). TerraBrasilis: A Spatial Data Infrastructure for Disseminating Deforestation Data From Brazil. Proceedings of the XIX Brazilian Symposium on Remote Sensing, Santos, Brazil."},{"key":"ref_29","unstructured":"Infrastructures, D.S.D. (2019, November 01). The SDI Cookbook. Available online: http:\/\/gsdiassociation.org\/images\/publications\/cookbooks\/SDI_Cookbook_GSDI_2004_ver2.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vinhas, L., de Queiroz, G.R., Ferreira, K.R., and Camara, G. (2016). Web services for big earth observation data. Revista Brasileira de Cartografia, 69.","DOI":"10.14393\/rbcv69n5-44004"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.14778\/2536222.2536227","article-title":"Hadoop gis: A high performance spatial data warehousing system over mapreduce","volume":"6","author":"Aji","year":"2013","journal-title":"Proc. VLDB Endow."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10619-012-7109-z","article-title":"MD-HBase: Design and implementation of an elastic data infrastructure for cloud-scale location services","volume":"31","author":"Nishimura","year":"2013","journal-title":"Distrib. Parallel Databases"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Eldawy, A., and Mokbel, M.F. (2015, January 13\u201317). Spatialhadoop: A mapreduce framework for spatial data. Proceedings of the 2015 IEEE 31st International Conference on Data Engineering (ICDE), Seoul, Korea.","DOI":"10.1109\/ICDE.2015.7113382"},{"key":"ref_35","unstructured":"Sweeney, C., Liu, L., Arietta, S., and Lawrence, J. (2011). HIPI: A Hadoop Image Processing Interface for Image-based MapReduce Tasks, University of Virginia. Technical Report."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Assis, L., Queiroz, G., Ferreira, K., Vinhas, L., Llapa, E., Sanchez, A., Maus, V., and Camara, G. (2016). Big data streaming for remote sensing time series analytics using MapReduce. Proceedings of the XVII Brazilian Symposium on GeoInformatics, Brazilian Journal of Cartography.","DOI":"10.14393\/rbcv69n5-44011"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, Q., Yang, C., Nebert, D., Liu, K., and Wu, H. (2010, January 2). Cloud computing for geosciences: Deployment of GEOSS clearinghouse on Amazon\u2019s EC2. Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869692.1869699"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.compenvurbsys.2016.10.010","article-title":"Utilizing cloud computing to address big geospatial data challenges","volume":"61","author":"Yang","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/17538947.2013.769783","article-title":"Redefining the possibility of digital Earth and geosciences with spatial cloud computing","volume":"6","author":"Yang","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MCSE.2013.19","article-title":"SciDB: A database management system for applications with complex analytics","volume":"15","author":"Stonebraker","year":"2013","journal-title":"Comput. Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Brown, P.G. (2010, January 6\u201310). Overview of SciDB: Large Scale Array Storage, Processing and Analysis. Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, Indianapolis, IN, USA.","DOI":"10.1145\/1807167.1807271"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1145\/276305.276386","article-title":"The multidimensional database system RasDaMan","volume":"27","author":"Baumann","year":"1998","journal-title":"ACM SIGMOD Rec."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Planthaber, G., Stonebraker, M., and Frew, J. (2012, January 6). EarthDB: Scalable analysis of MODIS data using SciDB. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Redondo Beach, CA, USA.","DOI":"10.1145\/2447481.2447483"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Krcal, L., and Ho, S. (2015, January 3\u20136). A SciDB-based Framework for Efficient Satellite Data Storage and Query based on Dynamic Atmospheric Event Trajectory. Proceedings of the 4th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Bellevue, WA, USA.","DOI":"10.1145\/2835185.2835190"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Baumann, P., Misev, D., Merticariu, V., Huu, B.P., and Bell, B. (2018, January 22\u201327). Datacubes: A Technology Survey. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518920"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","unstructured":"Killough, B. (2018, January 22\u201327). Overview of the Open Data Cube Initiative. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517694"},{"key":"ref_48","unstructured":"Lehto, L., K\u00e4hk\u00f6nen, J., Oksanen, J., and Sarjakoski, T. (2018, January 25\u201329). GeoCubes Finland\u2014A Unified Approach for Managing Multi-resolution Raster Geodata in a National Geospatial Research Infrastructure. Proceedings of the International Conference on Advanced Geographic Information Systems, Applications, and Services, Rome, Italy."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/20964471.2019.1611175","article-title":"Big Earth data analytics: A survey","volume":"3","author":"Yang","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_50","unstructured":"Pressman, R.S. (2005). Software Engineering: A Practitioner\u2019s Approach, Palgrave Macmillan."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wilkinson, L. (2012). The grammar of graphics. Handbook of Computational Statistics, Springer.","DOI":"10.1007\/978-3-642-21551-3_13"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1198\/jcgs.2009.07098","article-title":"A layered grammar of graphics","volume":"19","author":"Wickham","year":"2010","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_53","unstructured":"Tanenbaum, A.S., and Van Steen, M. (2007). Distributed Systems: Principles and Paradigms, Prentice-Hall."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/513\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:33:55Z","timestamp":1760189635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/513"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,12]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["ijgi8110513"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8110513","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,12]]}}}