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GI_Forum 2018, Volume 6, Issue 1Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2018, Volume 6, Issue 1, pp. 214-227, 2018/06/22
Journal for Geographic Information Science
Freely and openly available, remotely-sensed Earth observation (EO) data are rapidly developing in terms of increased geo-spatial resolution and temporal frequency. This type of data requires automated workflows for handling, processing and analysis, including methods to convert data into valid information. This study presents a proof-of-concept implementation of a generic, semantic EO data cube with automated daily integration and semantic enrichment of Sentinel-2 data. The paper focuses on the technical implementation of an automated dataflow that enables semantic queries in an EO data cube. It proposes a transferable analytical environment with analysis-ready data to facilitate research on replicable extraction of EO-based indicators. Application-independent semantic data cubes can facilitate monitoring of land cover changes and the development of transferable, generic EO-based indicators to support decision-makers in the humanitarian sector, and international initiatives, such as the United Nations’ Sustainable Development Goals. Syria was chosen as the study area because of the on-going conflict and humanitarian demand, paired with the relatively cloud-free climate’s suitability for optical EO time-series analysis.
Keywords: remote sensing, Big Earth Data, data cube, semantic enrichment, reproducible