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dc.contributor.authorSchwarz, Gottfried
dc.contributor.authorLorenzo, Jose
dc.contributor.authorCastel, Fabien
dc.contributor.authorDatcu, Mihai
dc.contributor.authorOctavian Dumitru, Corneliu
dc.date.accessioned2021-06-02T10:11:50Z
dc.date.available2021-06-02T10:11:50Z
dc.date.issued2019
dc.identifierONIX_20210602_10.5772/intechopen.86886_420
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/49306
dc.description.abstractThis chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::U Computing & information technology
dc.subject.otherEarth observation, machine learning, data mining, Copernicus Programme, TerraSAR-X
dc.titleChapter Artificial Intelligence Data Science Methodology for Earth Observation
dc.typechapter
oapen.identifier.doi10.5772/intechopen.86886
oapen.relation.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isFundedByH2020-EO-2017
oapen.grant.number776193
oapen.grant.acronymCANDELA


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