doi:10.1038/s41597-023-02096-0>) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'.">

rsi: Efficiently Retrieve and Process Satellite Imagery (original) (raw)

Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'.

Version: 0.3.2
Depends: R (≥ 4.0)
Imports: future.apply, glue, httr, jsonlite, lifecycle, proceduralnames, rlang, rstac, sf, terra, tibble
Suggests: curl, knitr, progressr, rmarkdown, testthat (≥ 3.0.0), withr
Published: 2025-01-22
DOI: 10.32614/CRAN.package.rsi
Author: Michael Mahoney ORCID iD [aut, cre], Felipe Carvalho [rev] (Felipe reviewed the package (v. 0.3.0) for rOpenSci, see https://github.com/ropensci/software-review/issues/636), Michael Sumner [rev] (Michael reviewed the package (v. 0.3.0) for rOpenSci, see https://github.com/ropensci/software-review/issues/636), Permian Global [cph, fnd]
Maintainer: Michael Mahoney <mike.mahoney.218 at gmail.com>
BugReports: https://github.com/Permian-Global-Research/rsi/issues
License: Apache License (≥ 2)
URL: https://github.com/Permian-Global-Research/rsi,https://permian-global-research.github.io/rsi/
NeedsCompilation: no
Citation: rsi citation info
Materials: README, NEWS
In views: Spatial
CRAN checks: rsi results

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