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Open AccessArticle

Semantic Earth Observation Data Cubes

1
Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
2
Italian Space Agency (ASI), 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Data 2019, 4(3), 102; https://doi.org/10.3390/data4030102
Received: 15 June 2019 / Revised: 12 July 2019 / Accepted: 15 July 2019 / Published: 17 July 2019
(This article belongs to the Special Issue Earth Observation Data Cubes)
There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data. View Full-Text
Keywords: remote sensing; big Earth data; big EO data; information extraction; semantic enrichment; time-series remote sensing; big Earth data; big EO data; information extraction; semantic enrichment; time-series
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Augustin, H.; Sudmanns, M.; Tiede, D.; Lang, S.; Baraldi, A. Semantic Earth Observation Data Cubes. Data 2019, 4, 102.

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