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Artificial Intelligence Data Science Methodology for Earth Observation

Dumitru, Corneliu Octavian and Schwarz, Gottfried and Castel, Fabien and Lorenzo, Jose and Datcu, Mihai (2019) Artificial Intelligence Data Science Methodology for Earth Observation. In: Advanced Analytics and Artificial Intelligence Applications pp. 1-20.

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Official URL: https://www.intechopen.com/online-first/artificial-intelligence-data-science-methodology-for-earth-observation

Abstract

This 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.

Item URL in elib:https://elib.dlr.de/129122/
Document Type:Book Section
Title:Artificial Intelligence Data Science Methodology for Earth Observation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deUNSPECIFIED
Castel, FabienATOS France SAUNSPECIFIED
Lorenzo, JoseATOS Spain SAUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:Advanced Analytics and Artificial Intelligence Applications
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.5772/intechopen.86886
Page Range:pp. 1-20
Status:Published
Keywords:Earth Observation, machine learning, data mining, Copernicus Programme, TerraSAR-X
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:26 Sep 2019 11:08
Last Modified:26 Sep 2019 11:08

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