From 90f8697c03a4c1c147c4cd496512cd68c4bc6462 Mon Sep 17 00:00:00 2001 From: Felipe Date: Thu, 23 May 2024 17:14:48 +0000 Subject: [PATCH] add rOpenSci badge in README --- README.Rmd | 1 + README.md | 94 +++++++++++++++++++++++++----------------------------- 2 files changed, 45 insertions(+), 50 deletions(-) diff --git a/README.Rmd b/README.Rmd index 82ea71aa8..d6ada3c68 100644 --- a/README.Rmd +++ b/README.Rmd @@ -24,6 +24,7 @@ torch::torch_manual_seed(1234) +[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/596_status.svg)](https://github.com/ropensci/software-review/issues/596) [![CRAN status](https://www.r-pkg.org/badges/version/sits)](https://cran.r-project.org/package=sits) [![R-check-dev](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml) [![Codecov](https://codecov.io/gh/e-sensing/sits/branch/dev/graph/badge.svg?token=hZxdJgKGcE)](https://codecov.io/gh/e-sensing/sits) diff --git a/README.md b/README.md index 79d43eeaf..74d9aad81 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,8 @@ Cubes +[![Status at rOpenSci Software Peer +Review](https://badges.ropensci.org/596_status.svg)](https://github.com/ropensci/software-review/issues/596) [![CRAN status](https://www.r-pkg.org/badges/version/sits)](https://cran.r-project.org/package=sits) [![R-check-dev](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml) @@ -119,7 +121,7 @@ devtools::install_github("e-sensing/sits", dependencies = TRUE) # load the sits library library(sits) #> SITS - satellite image time series analysis. -#> Loaded sits v1.4.2-1. +#> Loaded sits v1.5.0. #> See ?sits for help, citation("sits") for use in publication. #> Documentation avaliable in https://e-sensing.github.io/sitsbook/. ``` @@ -137,8 +139,8 @@ more information on how to install the required drivers. ### Image Collections Accessible by `sits` Users create data cubes from analysis-ready data (ARD) image collections -available in cloud services. The collections accessible in `sits` -1.4.2.1 are: +available in cloud services. The collections accessible in `sits` 1.5.0 +are: 1. Brazil Data Cube ([BDC](http://brazildatacube.org/en/home-page-2/#dataproducts)): @@ -174,13 +176,13 @@ similar ways. ``` r s2_cube <- sits_cube( - source = "MPC", - collection = "SENTINEL-2-L2A", - tiles = c("20LKP", "20LLP"), - bands = c("B03", "B08", "B11", "SCL"), - start_date = as.Date("2018-07-01"), - end_date = as.Date("2019-06-30"), - progress = FALSE + source = "MPC", + collection = "SENTINEL-2-L2A", + tiles = c("20LKP", "20LLP"), + bands = c("B03", "B08", "B11", "SCL"), + start_date = as.Date("2018-07-01"), + end_date = as.Date("2019-06-30"), + progress = FALSE ) ``` @@ -208,11 +210,11 @@ Pebesma, 2019](https://www.mdpi.com/2306-5729/4/3/92). ``` r gc_cube <- sits_regularize( - cube = s2_cube, - output_dir = tempdir(), - period = "P15D", - res = 60, - multicores = 4 + cube = s2_cube, + output_dir = tempdir(), + period = "P15D", + res = 60, + multicores = 4 ) ``` @@ -247,16 +249,16 @@ library(sits) data_dir <- system.file("extdata/raster/mod13q1", package = "sits") # create a cube from downloaded files raster_cube <- sits_cube( - source = "BDC", - collection = "MOD13Q1-6", - data_dir = data_dir, - delim = "_", - parse_info = c("X1", "X2", "tile", "band", "date"), - progress = FALSE + source = "BDC", + collection = "MOD13Q1-6", + data_dir = data_dir, + delim = "_", + parse_info = c("X1", "X2", "tile", "band", "date"), + progress = FALSE ) # obtain a set of samples defined by a CSV file csv_file <- system.file("extdata/samples/samples_sinop_crop.csv", - package = "sits" + package = "sits" ) # retrieve the time series associated with the samples from the data cube points <- sits_get_data(raster_cube, samples = csv_file) @@ -311,16 +313,16 @@ data("samples_modis_ndvi") data("point_mt_6bands") # Train a deep learning model tempcnn_model <- sits_train( - samples = samples_modis_ndvi, - ml_method = sits_tempcnn() + samples = samples_modis_ndvi, + ml_method = sits_tempcnn() ) # Select NDVI band of the point to be classified # Classify using TempCNN model # Plot the result -point_mt_6bands |> - sits_select(bands = "NDVI") |> - sits_classify(tempcnn_model) |> - plot() +point_mt_6bands |> + sits_select(bands = "NDVI") |> + sits_classify(tempcnn_model) |> + plot() #> | | | 0% | |=================================== | 50% | |======================================================================| 100% ``` @@ -342,44 +344,36 @@ using `sits_view()`. # Cube is composed of MOD13Q1 images from the Sinop region in Mato Grosso (Brazil) data_dir <- system.file("extdata/raster/mod13q1", package = "sits") sinop <- sits_cube( - source = "BDC", - collection = "MOD13Q1-6", - data_dir = data_dir, - delim = "_", - parse_info = c("X1", "X2", "tile", "band", "date"), - progress = FALSE + source = "BDC", + collection = "MOD13Q1-6", + data_dir = data_dir, + delim = "_", + parse_info = c("X1", "X2", "tile", "band", "date"), + progress = FALSE ) # Classify the raster cube, generating a probability file # Filter the pixels in the cube to remove noise probs_cube <- sits_classify( - data = sinop, - ml_model = tempcnn_model, - output_dir = tempdir() + data = sinop, + ml_model = tempcnn_model, + output_dir = tempdir() ) #> | | | 0% | |======================================================================| 100% # apply a bayesian smoothing to remove outliers bayes_cube <- sits_smooth( - cube = probs_cube, - output_dir = tempdir() + cube = probs_cube, + output_dir = tempdir() ) # generate a thematic map label_cube <- sits_label_classification( - cube = bayes_cube, - output_dir = tempdir() + cube = bayes_cube, + output_dir = tempdir() ) #> | | | 0% | |======================================================================| 100% # plot the the labelled cube plot(label_cube, - title = "Land use and Land cover in Sinop, MT, Brazil in 2018" + title = "Land use and Land cover in Sinop, MT, Brazil in 2018" ) -#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package, -#> which was just loaded, will retire in October 2023. -#> Please refer to R-spatial evolution reports for details, especially -#> https://r-spatial.org/r/2023/05/15/evolution4.html. -#> It may be desirable to make the sf package available; -#> package maintainers should consider adding sf to Suggests:. -#> The sp package is now running under evolution status 2 -#> (status 2 uses the sf package in place of rgdal) ```