plot overviews

DESCRIPTION

@@ -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

NAMESPACE

@@ -274,8 +274,6 @@ 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)

R/api_gdal.R

@@ -92,22 +92,30 @@
  )
  return(invisible(file))
 }
-#' @title Run gdal_warp for SAR GRD files
+#' @title Run gdal_warp_file
 #' @noRd
 #' @param raster_file File to be copied from (with path)
-#' @param size Size of output file
+#' @param sizes Sizes of output file
+#' @param t_srs Target spatial reference system
 #' @returns Name of output file
-.gdal_warp_grd <- function(raster_file, size) {
+.gdal_warp_file <- function(raster_file, sizes, t_srs = NULL) {
+ # create a temporary file
  temp_file <- tempfile(fileext = ".tif")
+ # basic parameters
+ params = list(
+ "-ts" = list(sizes[["xsize"]], sizes[["ysize"]]),
+ "-multi" = FALSE,
+ "-q" = TRUE,
+ "-overwrite" = FALSE
+ )
+ # additional param for target SRS
+ if (.has(t_srs))
+ params <- append(params, c("t_srs" = t_srs))
+ # warp the data
  .gdal_warp(
  file = temp_file,
  base_files = raster_file,
- params = list(
- "-ts" = list(size[["xsize"]], size[["ysize"]]),
- "-multi" = FALSE,
- "-q" = TRUE,
- "-overwrite" = FALSE
- ),
+ params = params,
  quiet = TRUE)
  return(temp_file)
 }

R/api_plot_raster.R

@@ -38,7 +38,7 @@
  band_offset <- .offset(band_conf)
  max_value <- .max_value(band_conf)
  # retrieve the overview if COG
- bw_file <- .gdal_warp_grd(bw_file, sizes)
+ bw_file <- .gdal_warp_file(bw_file, sizes)
  # open the file in terra
  rast <- terra::rast(bw_file)
  # apply scale and offset
@@ -142,9 +142,9 @@
  max_value <- .max_value(band_params)
  # used for SAR images without tiling system
  if (tile[["tile"]] == "NoTilingSystem") {
- red_file <- .gdal_warp_grd(red_file, sizes)
- green_file <- .gdal_warp_grd(green_file, sizes)
- blue_file <- .gdal_warp_grd(blue_file, sizes)
+ red_file <- .gdal_warp_file(red_file, sizes)
+ green_file <- .gdal_warp_file(green_file, sizes)
+ blue_file <- .gdal_warp_file(blue_file, sizes)
  }
  # plot multitemporal band as RGB
  p <- .plot_rgb_stars(
@@ -201,9 +201,9 @@
  sizes <- .tile_overview_size(tile = tile, max_size)
  # used for SAR images
  if (tile[["tile"]] == "NoTilingSystem") {
- red_file <- .gdal_warp_grd(red_file, sizes)
- green_file <- .gdal_warp_grd(green_file, sizes)
- blue_file <- .gdal_warp_grd(blue_file, sizes)
+ red_file <- .gdal_warp_file(red_file, sizes)
+ green_file <- .gdal_warp_file(green_file, sizes)
+ blue_file <- .gdal_warp_file(blue_file, sizes)
  }
  p <- .plot_rgb_stars(
  red_file = red_file,