Merge branch 'e-sensing:master' into master

.github/workflows/R-CMD-check.yaml

@@ -2,9 +2,9 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [dev]
+    branches: [master, dev]
   pull_request:
-    branches: [dev]
+    branches: [master, dev]
 
 name: R-CMD-check
 

README.Rmd

@@ -168,7 +168,7 @@ raster_cube <- sits_cube(
     collection = "MOD13Q1-6",
     data_dir = data_dir,
     delim = "_",
-    parse_info = c("X1", "tile", "band", "date"),
+    parse_info = c("X1", "X2", "tile", "band", "date"),
     progress = FALSE
 )
 # obtain a set of samples defined by a CSV file
@@ -240,7 +240,7 @@ sinop <- sits_cube(
     collection = "MOD13Q1-6",
     data_dir = data_dir,
     delim = "_",
-    parse_info = c("X1", "tile", "band", "date"),
+    parse_info = c("X1", "X2", "tile", "band", "date"),
     progress = FALSE
 )
 # Classify the raster cube, generating a probability file

README.md

@@ -11,7 +11,6 @@ Cubes
 
 [![CRAN
 status](https://www.r-pkg.org/badges/version/sits)](https://cran.r-project.org/package=sits)
-[![Total downloads](http://cranlogs.r-pkg.org/badges/grand-total/sits?color=blue)](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)
 [![Documentation](https://img.shields.io/badge/docs-online-blueviolet)](https://e-sensing.github.io/sitsbook/)
@@ -108,9 +107,9 @@ devtools::install_github("e-sensing/sits", dependencies = TRUE)
 # load the sits library
 library(sits)
 #> SITS - satellite image time series analysis.
-#> Loaded sits v1.3.0.
+#> Loaded sits v1.4.0.
 #>         See ?sits for help, citation("sits") for use in publication.
-#>         See demo(package = "sits") for examples.
+#>         Documentation avaliable in https://e-sensing.github.io/sitsbook/.
 ```
 
 ## Building Earth Observation Data Cubes
@@ -119,7 +118,7 @@ library(sits)
 
 The `sits` package allows users to created data cubes from
 analysis-ready data (ARD) image collections available in cloud services.
-The collections accessible in `sits` 1.3.0 are:
+The collections accessible in `sits` 1.4.0 are:
 
 1.  Brazil Data Cube
     ([BDC](http://brazildatacube.org/en/home-page-2/#dataproducts)):
@@ -230,7 +229,7 @@ raster_cube <- sits_cube(
   collection = "MOD13Q1-6",
   data_dir = data_dir,
   delim = "_",
-  parse_info = c("X1", "tile", "band", "date"),
+  parse_info = c("X1", "X2", "tile", "band", "date"),
   progress = FALSE
 )
 # obtain a set of samples defined by a CSV file
@@ -301,6 +300,7 @@ point_mt_6bands %>%
   sits_select(bands = "NDVI") %>%
   sits_classify(tempcnn_model) %>%
   plot()
+#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
 ```
 
 <div class="figure" style="text-align: center">
@@ -325,7 +325,7 @@ sinop <- sits_cube(
   collection = "MOD13Q1-6",
   data_dir = data_dir,
   delim = "_",
-  parse_info = c("X1", "tile", "band", "date"),
+  parse_info = c("X1", "X2", "tile", "band", "date"),
   progress = FALSE
 )
 # Classify the raster cube, generating a probability file
@@ -335,6 +335,7 @@ probs_cube <- sits_classify(
   ml_model = tempcnn_model,
   output_dir = tempdir()
 )
+#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
 # apply a bayesian smoothing to remove outliers
 bayes_cube <- sits_smooth(
   cube = probs_cube,
@@ -345,6 +346,7 @@ label_cube <- sits_label_classification(
   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"