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rsi: Efficiently Retrieve and Process Satellite Imagery
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: |
0.2.0 |
| 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: |
2024-03-29 |
| Author: |
Michael Mahoney 
[aut, cre],
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 |
| Materials: |
README NEWS |
| In views: |
Spatial |
| CRAN checks: |
rsi results |
Documentation:
Downloads:
Linking:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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