Merge pull request #1136 from e-sensing/dev

Pre-realease 1.5.0-1

Author: gilbertocamara

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sits
 Type: Package
-Version: 1.5.0
+Version: 1.5.0-1
 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'),
@@ -17,7 +17,7 @@ Description: An end-to-end toolkit for land use and land cover classification
     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 
+    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.
@@ -28,10 +28,12 @@ Description: An end-to-end toolkit for land use and land cover classification
     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) <doi:10.48550/arXiv.2007.00586>.
+    Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
     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 
+    methods for active learning and uncertainty assessment. Supports object-based 
+    time series analysis using package supercells <https://jakubnowosad.com/supercells/>.
+    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
@@ -58,7 +60,7 @@ Imports:
     sysfonts,
     slider (>= 0.2.0),
     stats,
-    terra (>= 1.7-71),
+    terra (>= 1.7-65),
     tibble (>= 3.1),
     tidyr (>= 1.2.0),
     torch (>= 0.11.0),
@@ -130,7 +132,9 @@ Collate:
     'api_cube.R'
     'api_data.R'
     'api_debug.R'
+    'api_detect_changes.R'
     'api_download.R'
+    'api_dtw.R'
     'api_environment.R'
     'api_factory.R'
     'api_file_info.R'
@@ -146,6 +150,7 @@ Collate:
     'api_mosaic.R'
     'api_opensearch.R'
     'api_parallel.R'
+    'api_patterns.R'
     'api_period.R'
     'api_plot_time_series.R'
     'api_plot_raster.R'
@@ -215,6 +220,9 @@ Collate:
     'sits_cube_copy.R'
     'sits_clean.R'
     'sits_cluster.R'
+    'sits_detect_change.R'
+    'sits_detect_change_method.R'
+    'sits_dtw.R'
     'sits_factory.R'
     'sits_filters.R'
     'sits_geo_dist.R'

NAMESPACE

@@ -241,6 +241,7 @@ S3method(.tile_ncols,raster_cube)
 S3method(.tile_nrows,default)
 S3method(.tile_nrows,raster_cube)
 S3method(.tile_path,default)
+S3method(.tile_path,derived_cube)
 S3method(.tile_path,raster_cube)
 S3method(.tile_paths,default)
 S3method(.tile_paths,raster_cube)
@@ -274,8 +275,6 @@ S3method(.view_add_overlay_grps,class_cube)
 S3method(.view_add_overlay_grps,derived_cube)
 S3method(.view_add_overlay_grps,raster_cube)
 S3method(.view_add_overlay_grps,vector_cube)
-S3method(.view_adjust_palette,default)
-S3method(.view_adjust_palette,sar_cube)
 S3method(plot,class_cube)
 S3method(plot,class_vector_cube)
 S3method(plot,geo_distances)

NEWS.md

@@ -1,6 +1,17 @@
 # SITS Release History
 
 # What's new in SITS version 1.5
+
+### Hotfix version 1.5.0-1
+* Add multicores processing support for active learning sampling methods
+* Remove tapply from `.reg_cube_split_assets()` for R 4.X compatibility
+* Fix `sits_merge()` function that was not merging `SAR` and `OPTICAL` cubes
+* Rename n_input_pixels back to input_pixels for compatibility with models trained in old versions of the package
+* Fix torch usage in Apple M3 by turning off MPS technology
+* Fix date parameter usage in `sits_view()`
+* Improve `plot()` performance using raster overviews
+* Include support for PLANET Mosaic product
+
 ### New features in SITS version 1.5.0
 * Support for SENTINEL-1-RTC and SENTINEL-2-L2A in CDSE
 * Include support for DEA products SENTINEL-1-RTC, LS5-SR, LS7-SR, LS9-SR, ALOS-PALSAR-MOSAIC, NDVI ANOMALY, DAILY CHIRPS, MONTHLY CHIRPS and DEM-30

R/RcppExports.R

@@ -9,6 +9,10 @@ weighted_uncert_probs <- function(data_lst, unc_lst) {
     .Call(`_sits_weighted_uncert_probs`, data_lst, unc_lst)
 }
 
+dtw_distance <- function(ts1, ts2) {
+    .Call(`_sits_dtw_distance`, ts1, ts2)
+}
+
 C_kernel_median <- function(x, ncols, nrows, band, window_size) {
     .Call(`_sits_C_kernel_median`, x, ncols, nrows, band, window_size)
 }

R/api_check.R

@@ -1364,14 +1364,14 @@
 #' @title Does the result have the same number of pixels as the input values?
 #' @name .check_processed_values
 #' @param values a matrix of processed values
-#' @param n_input_pixels number of pixels in input matrix
+#' @param input_pixels number of pixels in input matrix
 #' @return Called for side effects.
 #' @keywords internal
 #' @noRd
-.check_processed_values <- function(values, n_input_pixels) {
+.check_processed_values <- function(values, input_pixels) {
     .check_set_caller(".check_processed_values")
     .check_that(
-        !(is.null(nrow(values))) && nrow(values) == n_input_pixels
+        !(is.null(nrow(values))) && nrow(values) == input_pixels
     )
     return(invisible(values))
 }

R/api_classify.R

@@ -115,7 +115,7 @@
         # Fill with zeros remaining NA pixels
         values <- C_fill_na(values, 0)
         # Used to check values (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Log here
         .debug_log(
             event = "start_block_data_classification",
@@ -127,7 +127,7 @@
         # Are the results consistent with the data input?
         .check_processed_values(
             values = values,
-            n_input_pixels = n_input_pixels
+            input_pixels = input_pixels
         )
         # Log
         .debug_log(

R/api_combine_predictions.R

@@ -219,13 +219,13 @@
     # Average probability calculation
     comb_fn <- function(values, uncert_values = NULL) {
         # Check values length
-        n_input_pixels <- nrow(values[[1]])
+        input_pixels <- nrow(values[[1]])
         # Combine by average
         values <- weighted_probs(values, weights)
         # get the number of labels
         n_labels <- length(sits_labels(cubes[[1]]))
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         .check_processed_labels(values, n_labels)
         # Return values
         values
@@ -244,13 +244,13 @@
     # Average probability calculation
     comb_fn <- function(values, uncert_values) {
         # Check values length
-        n_input_pixels <- nrow(values[[1]])
+        input_pixels <- nrow(values[[1]])
         # Combine by average
         values <- weighted_uncert_probs(values, uncert_values)
         # get the number of labels
         n_labels <- length(sits_labels(cubes[[1]]))
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         .check_processed_labels(values, n_labels)
         # Return values
         values