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Open AccessArticle

On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library

Institute for Geoinformatics, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
Author to whom correspondence should be addressed.
Received: 29 May 2019 / Revised: 25 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Earth Observation Data Cubes)
PDF [6166 KB, uploaded 28 June 2019]


Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed. View Full-Text
Keywords: earth observations; satellite imagery; R; data cubes; Sentinel-2 earth observations; satellite imagery; R; data cubes; Sentinel-2

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Appel, M.; Pebesma, E. On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library. Data 2019, 4, 92.

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