Imperial College London
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ProfessorWouterBuytaert

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor in Hydrology and Water Resources
 
 
 
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Contact

 

+44 (0)20 7594 1329w.buytaert Website

 
 
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Assistant

 

Miss Judith Barritt +44 (0)20 7594 5967

 
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Location

 

403ASkempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Appel:2018:10.1016/j.isprsjprs.2018.01.014,
author = {Appel, M and Lahn, F and Buytaert, W and Pebesma, E},
doi = {10.1016/j.isprsjprs.2018.01.014},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
pages = {47--56},
title = {Open and scalable analytics of large Earth observation datasets: from scenes to multidimensional arrays using SciDB and GDAL},
url = {http://dx.doi.org/10.1016/j.isprsjprs.2018.01.014},
volume = {138},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth's surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend the geospatial data abstraction library (GDAL) and the array-based data management and analytics system SciDB. We present an approach to automatically convert collections of scenes to multidimensional arrays and use SciDB to scale computationally intensive analytics. We evaluate the approach in three study cases on national scale land use change monitoring with Landsat imagery, global empirical orthogonal function analysis of daily precipitation, and combining historical climate model projections with satellite-based observations. Results indicate that the approach can be used to represent various EO datasets and that analyses in SciDB scale well with available computational resources. To simplify analyses of higher-dimensional datasets as from climate model output, however, a generalization of the GDAL data model might be needed. All parts of this work have been implemented as open-source software and we discuss how this may facilitate open and reproducible EO analyses.
AU  - Appel,M
AU  - Lahn,F
AU  - Buytaert,W
AU  - Pebesma,E
DO  - 10.1016/j.isprsjprs.2018.01.014
EP  - 56
PY  - 2018///
SN  - 0924-2716
SP  - 47
TI  - Open and scalable analytics of large Earth observation datasets: from scenes to multidimensional arrays using SciDB and GDAL
T2  - ISPRS Journal of Photogrammetry and Remote Sensing
UR  - http://dx.doi.org/10.1016/j.isprsjprs.2018.01.014
UR  - http://hdl.handle.net/10044/1/57417
VL  - 138
ER  -
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