Earth Observation Data Cubes from Satellite Image Collections

Processing collections of Earth observation images as on-demand multispectral, multitemporal data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, materializing data cubes as 'netCDF' files, and plotting. User-defined 'R' functions can be applied over chunks of data cubes. The package implements lazy evaluation and multithreading. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries.


Reference manual

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0.1.0 by Marius Appel, 2 months ago

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Authors: Marius Appel [aut, cre] , Edzer Pebesma [ctb] , Lewis Van Winkle [cph] , Ole Christian Eidheim [cph] , Niels Lohmann [cph] , Howard Hinnant [cph] , Adrian Colomitchi [cph] , Florian Dang [cph] , Paul Thompson [cph] , Tomasz KamiƄski [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Depends on Rcpp, RcppProgress, jsonlite, ncdf4

Suggests knitr, magrittr, processx, rmarkdown, stars

Linking to Rcpp, RcppProgress

System requirements: cxx11, gdal, libgdal, libproj, libcurl, netcdf4

See at CRAN