SITS: Satellite Image Time Series Analysis for Earth Observation Data Cubes (submit R package for review)

gilbertocamara commented 1 year ago

Submitting Author Name: Gilberto Camara Submitting Author Github Handle: !--author1-->@gilbertocamara@mpadge<!--end-editor-- Reviewers: @mikemahoney218, @paleolimbot

Due date for @mikemahoney218: 2024-08-07 Due date for @paleolimbot: 2024-08-07

Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: sits
Type: Package
Version: 1.4.2
Title: Satellite Image Time Series Analysis for Earth Observation Data Cubes
Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = 'rolf.simoes@inpe.br'),
         person('Gilberto', 'Camara', role = c('aut', 'cre'), email = 'gilberto.camara.inpe@gmail.com'),
         person('Felipe', 'Souza', role = c('aut'), email = 'felipe.carvalho@inpe.br'),
         person('Lorena', 'Santos', role = c('aut'), email = 'lorena.santos@inpe.br'),
         person('Pedro', 'Andrade', role = c('aut'), email = 'pedro.andrade@inpe.br'),
         person('Karine', 'Ferreira', role = c('aut'), email = 'karine.ferreira@inpe.br'),
         person('Alber', 'Sanchez', role = c('aut'), email = 'alber.ipia@inpe.br'),
         person('Gilberto', 'Queiroz', role = c('aut'), email = 'gilberto.queiroz@inpe.br')
         )
Maintainer: Gilberto Camara <gilberto.camara.inpe@gmail.com>
Description: An end-to-end toolkit for land use and land cover classification
using big Earth observation data, based on machine learning methods 
applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, 
Brazil Data Cube, and Digital Earth Africa using the Spatio-temporal Asset Catalog (STAC) 
protocol (<https://stacspec.org/> and the 'gdalcubes' R package 
developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>.
Supports visualization methods for images and time series and 
smoothing filters for dealing with noisy time series.
Includes functions for quality assessment of training samples using self-organized maps 
as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. 
Provides machine learning methods including support vector machines, 
random forests, extreme gradient boosting, multi-layer perceptrons,
temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
by Garnot and Landrieu (2020) <arXiv:2007.00586>.
Performs efficient classification of big Earth observation data cubes and includes 
functions for post-classification smoothing based on Bayesian inference, and 
methods for uncertainty assessment. Enables best
practices for estimating area and assessing accuracy of land change as 
recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
URL: https://github.com/e-sensing/sits/, https://e-sensing.github.io/sitsbook/
BugReports: https://github.com/e-sensing/sits/issues
License: GPL-2
ByteCompile: true
LazyData: true
Imports:
yaml,
dplyr (>= 1.0.0),
gdalUtilities,
grDevices,
graphics,
lubridate,
parallel (>= 4.0.5),
purrr (>= 0.3.0),
Rcpp,
rstac (>= 0.9.2-3),
sf (>= 1.0-12),
slider (>= 0.2.0),
stats,
terra (>= 1.5-17),
tibble (>= 3.1),
tidyr (>= 1.2.0),
torch (>= 0.9.0),
utils
Suggests:
caret,
dendextend,
dtwclust,
DiagrammeR,
digest,
e1071,
exactextractr,
FNN,
future,
gdalcubes (>= 0.6.0),
geojsonsf,
ggplot2,
httr,
jsonlite,
kohonen (>= 3.0.11),
leafem (>= 0.2.0),
leaflet (>= 2.1.1),
luz (>= 0.3.0),
methods,
mgcv,
nnet,
openxlsx,
randomForest,
randomForestExplainer,
RcppArmadillo (>= 0.11),
scales,
stars (>= 0.6),
supercells,
testthat (>= 3.1.3),
tmap (>= 3.3),
torchopt (>= 0.1.2),
xgboost,
covr
Config/testthat/edition: 3
Config/testthat/parallel: false
Config/testthat/start-first: cube, raster, regularize, data, ml
LinkingTo:
Rcpp,
RcppArmadillo
RoxygenNote: 7.2.3
Collate: 
'api_accessors.R'
'api_accuracy.R'
'api_apply.R'
'api_band.R'
'api_bbox.R'
'api_block.R'
'api_check.R'
'api_chunks.R'
'api_classify.R'
'api_cluster.R'
'api_colors.R'
'api_combine_predictions.R'
'api_comp.R'
'api_conf.R'
'api_csv.R'
'api_cube.R'
'api_data.R'
'api_debug.R'
'api_download.R'
'api_factory.R'
'api_file_info.R'
'api_file.R'
'api_gdal.R'
'api_gdalcubes.R'
'api_imputation.R'
'api_jobs.R'
'api_label_class.R'
'api_mixture_model.R'
'api_ml_model.R'
'api_mosaic.R'
'api_parallel.R'
'api_period.R'
'api_plot_time_series.R'
'api_plot_raster.R'
'api_point.R'
'api_predictors.R'
'api_raster.R'
'api_raster_sub_image.R'
'api_raster_terra.R'
'api_reclassify.R'
'api_roi.R'
'api_samples.R'
'api_segments.R'
'api_sf.R'
'api_shp.R'
'api_signal.R'
'api_smooth.R'
'api_smote.R'
'api_som.R'
'api_source.R'
'api_source_aws.R'
'api_source_bdc.R'
'api_source_deafrica.R'
'api_source_hls.R'
'api_source_local.R'
'api_source_mpc.R'
'api_source_sdc.R'
'api_source_stac.R'
'api_source_usgs.R'
'api_space_time_operations.R'
'api_stac.R'
'api_stats.R'
'api_summary.R'
'api_tibble.R'
'api_tile.R'
'api_timeline.R'
'api_torch.R'
'api_torch_psetae.R'
'api_ts.R'
'api_tuning.R'
'api_uncertainty.R'
'api_utils.R'
'api_variance.R'
'api_view.R'
'RcppExports.R'
'data.R'
'sits-package.R'
'sits_apply.R'
'sits_accuracy.R'
'sits_active_learning.R'
'sits_bands.R'
'sits_bbox.R'
'sits_classify.R'
'sits_colors.R'
'sits_combine_predictions.R'
'sits_config.R'
'sits_csv.R'
'sits_cube.R'
'sits_cube_copy.R'
'sits_cluster.R'
'sits_factory.R'
'sits_filters.R'
'sits_geo_dist.R'
'sits_get_data.R'
'sits_labels.R'
'sits_label_classification.R'
'sits_lighttae.R'
'sits_machine_learning.R'
'sits_merge.R'
'sits_mixture_model.R'
'sits_mlp.R'
'sits_mosaic.R'
'sits_model_export.R'
'sits_patterns.R'
'sits_plot.R'
'sits_predictors.R'
'sits_reclassify.R'
'sits_regularize.R'
'sits_resnet.R'
'sits_sample_functions.R'
'sits_segmentation.R'
'sits_select.R'
'sits_sf.R'
'sits_smooth.R'
'sits_som.R'
'sits_summary.R'
'sits_tae.R'
'sits_tempcnn.R'
'sits_timeline.R'
'sits_train.R'
'sits_tuning.R'
'sits_utils.R'
'sits_uncertainty.R'
'sits_validate.R'
'sits_view.R'
'sits_values.R'
'sits_variance.R'
'sits_xlsx.R'
'zzz.R'

Scope

Please indicate which category or categories from our package fit policies this package falls under:
- [X ] geospatial data
Explain how and why the package falls under these categories (briefly, 1-2 sentences): sits is a package for satellite image time series analysis, that works with big Earth observation data sets.
Who is the target audience and what are scientific applications of this package? The target audience is made of remote sensing and environmental experts that want to classify remote sensing images for applications such as deforestation detection, agricultural and land use/land cover mapping, biodiversity conservation, and land degradation monitoring.,
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? There are currently no other open source software packages that have the same capabilities.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research? Not applicable
If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted. Not applicable
Explain reasons for any pkgcheck items which your package is unable to pass.

(a) Vignettes: instead of preparing vignettes, the authors have written an on-line book that describes the contents of the package in detail. The book is available at the URL https://e-sensing.github.io/sitsbook/

Important notes:

(1) To run the tests, examples, and code coverage, please make sure the following environment variables are set in the R session: Sys.setenv("SITS_RUN_TESTS" = "YES") Sys.setenv("SITS_RUN_EXAMPLES" = "YES") sits is a fairly large package, and the tests take a long time to run, since they access cloud services. For this reason, testing needs to be manually enabled.

(2) Please review version 1.4.2, not yet on CRAN, which is available in the "dev" branch in the github repository.

Technical checks

Confirm each of the following by checking the box.

[x] I have read the rOpenSci packaging guide.
[x] I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has a CRAN and OSI accepted license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions, created with roxygen2.
[x] contains a vignette with examples of its essential functions and uses (see above).
[x] has a test suite.
[x] has continuous integration, including reporting of test coverage.

Publication options

[x] Do you intend for this package to go on CRAN? The package is already on CRAN.
[ ] Do you intend for this package to go on Bioconductor?
[ x] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

- [x] The package is novel and will be of interest to the broad readership of the journal. - [x] The manuscript describing the package is no longer than 3000 words. - [x] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see [MEE's Policy on Publishing Code](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/journal-resources/policy-on-publishing-code.html)) - (*Scope: Do consider MEE's [Aims and Scope](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/aims-and-scope/read-full-aims-and-scope.html) for your manuscript. We make no guarantee that your manuscript will be within MEE scope.*) - (*Although not required, we strongly recommend having a full manuscript prepared when you submit here.*) - (*Please do not submit your package separately to Methods in Ecology and Evolution*)

Code of conduct

[x] I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

gilbertocamara commented 4 months ago

Dear @jooolia and @mpadge

Many thanks! Much appreciated!

mpadge commented 4 months ago

@ropensci-review-bot seeking reviewers

ropensci-review-bot commented 4 months ago

Please add this badge to the README of your package repository:

[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/596_status.svg)](https://github.com/ropensci/software-review/issues/596)

Furthermore, if your package does not have a NEWS.md file yet, please create one to capture the changes made during the review process. See https://devguide.ropensci.org/releasing.html#news

gilbertocamara commented 4 months ago

Thanks! Included rOpenSci badge in sits github site.

mpadge commented 3 months ago

@ropensci-review-bot assign @mikemahoney218 as reviewer