From 542192aa9e7448c03a086fc548ec03c8a232f124 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Sat, 7 Dec 2024 20:03:21 +0000
Subject: [PATCH 01/10] fix bug in time series classification images

---
 R/api_classify.R  | 4 ++++
 R/api_ml_model.R  | 2 ++
 R/sits_classify.R | 2 +-
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/R/api_classify.R b/R/api_classify.R
index 5762c5f5..86f7b570 100755
--- a/R/api_classify.R
+++ b/R/api_classify.R
@@ -651,6 +651,8 @@
         values <- .pred_features(pred_part)
         # Classify
         values <- ml_model(values)
+        # normalize and calibrate values
+        values <- .ml_normalize(ml_model, values)
         # Return classification
         values <- tibble::as_tibble(values)
         values
@@ -691,6 +693,8 @@
         values <- .pred_features(pred_part)
         # Classify
         values <- ml_model(values)
+        # normalize and calibrate values
+        values <- .ml_normalize(ml_model, values)
         # Return classification
         values <- tibble::tibble(data.frame(values))
         # Clean GPU memory
diff --git a/R/api_ml_model.R b/R/api_ml_model.R
index 3d4df02b..d480f964 100644
--- a/R/api_ml_model.R
+++ b/R/api_ml_model.R
@@ -119,8 +119,10 @@
 #' @export
 #'
 .ml_normalize.torch_model <- function(ml_model, values){
+    column_names <- colnames(values)
     values[is.na(values)] <- 0
     values <- softmax(values)
+    colnames(values) <- column_names
     return(values)
 }
 #' @export
diff --git a/R/sits_classify.R b/R/sits_classify.R
index ee603458..fd712ec6 100644
--- a/R/sits_classify.R
+++ b/R/sits_classify.R
@@ -437,7 +437,7 @@ sits_classify.segs_cube <- function(data,
         proc_bloat <- .conf("processing_bloat_gpu")
     }
     # avoid memory race in Apple MPS
-    if(.torch_mps_enabled(ml_model)){
+    if (.torch_mps_enabled(ml_model)) {
         memsize <- 1
         gpu_memory <- 1
     }

From 699baf0894ce33aed9e792f7cfd0b409468f4e62 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Sat, 7 Dec 2024 21:56:08 +0000
Subject: [PATCH 02/10] update .tile_extract message

---
 R/api_tile.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/api_tile.R b/R/api_tile.R
index df41654f..26fe7e4b 100644
--- a/R/api_tile.R
+++ b/R/api_tile.R
@@ -1465,7 +1465,8 @@ NULL
         x = r_obj,
         y = segments,
         fun = NULL,
-        include_cols = "pol_id"
+        include_cols = "pol_id",
+        progress = FALSE
     )
     values <- dplyr::bind_rows(values)
     values <- dplyr::select(values, -"coverage_fraction")

From e39ae1359aed141cf136893ead69e6a483541fb5 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Mon, 9 Dec 2024 19:24:46 +0000
Subject: [PATCH 03/10] add FLT8S metadata

---
 inst/extdata/config_internals.yml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/inst/extdata/config_internals.yml b/inst/extdata/config_internals.yml
index 3da8d367..bc4a603c 100644
--- a/inst/extdata/config_internals.yml
+++ b/inst/extdata/config_internals.yml
@@ -125,6 +125,13 @@ default_values       :
         maximum_value: 1.7014118346015974e+37
         offset_value : 0
         scale_factor : 1
+    FLT8S            :
+        data_type    : "FLT8S"
+        missing_value: -3.402823466385288e+37
+        minimum_value: -3.402823466385288e+37
+        maximum_value: 1.7014118346015974e+37
+        offset_value : 0
+        scale_factor : 1
 
 # Derived cube definitions
 derived_cube         :

From ae2e0eac005f1d81a5223c42536474ee773ba193 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Wed, 11 Dec 2024 20:33:51 +0000
Subject: [PATCH 04/10] fix bug in classify label's name with space

---
 R/api_classify.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/R/api_classify.R b/R/api_classify.R
index 86f7b570..a7a4647e 100755
--- a/R/api_classify.R
+++ b/R/api_classify.R
@@ -596,7 +596,8 @@
         prediction <- .classify_ts_gpu(
             pred = pred,
             ml_model = ml_model,
-            gpu_memory = gpu_memory)
+            gpu_memory = gpu_memory
+        )
     else
         prediction <- .classify_ts_cpu(
             pred = pred,
@@ -696,7 +697,7 @@
         # normalize and calibrate values
         values <- .ml_normalize(ml_model, values)
         # Return classification
-        values <- tibble::tibble(data.frame(values))
+        values <- tibble::as_tibble(values)
         # Clean GPU memory
         .ml_gpu_clean(ml_model)
         return(values)

From ef975f0d4672c5d0a8fe4bfd79839d103c366456 Mon Sep 17 00:00:00 2001
From: Felipe Carvalho <lipecaso@gmail.com>
Date: Thu, 12 Dec 2024 16:54:16 +0000
Subject: [PATCH 05/10] fix bug in plot segment with RGB

---
 R/sits_plot.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/R/sits_plot.R b/R/sits_plot.R
index dcfae4a2..4bcf0b8a 100644
--- a/R/sits_plot.R
+++ b/R/sits_plot.R
@@ -836,6 +836,8 @@ plot.vector_cube <- function(x, ...,
             sf_seg   = sf_seg,
             seg_color = seg_color,
             line_width = line_width,
+            first_quantile = first_quantile,
+            last_quantile = last_quantile,
             scale = scale,
             max_cog_size = max_cog_size,
             tmap_params = tmap_params

From ced2f92c3d9f40dab87a169cf7ab4902698d1530 Mon Sep 17 00:00:00 2001
From: gilbertocamara <gilberto.camara@inpe.br>
Date: Thu, 12 Dec 2024 22:19:49 -0300
Subject: [PATCH 06/10] add function to export time series to CSV

---
 NAMESPACE                     |  1 +
 R/api_csv.R                   | 19 +++++++++++++
 R/sits_classify.R             |  8 ++++--
 R/sits_csv.R                  | 50 +++++++++++++++++++++++++++++------
 man/sits_timeseries_to_csv.Rd | 33 +++++++++++++++++++++++
 5 files changed, 101 insertions(+), 10 deletions(-)
 create mode 100644 man/sits_timeseries_to_csv.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 754f776d..a8b4414c 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -601,6 +601,7 @@ export(sits_tae)
 export(sits_tempcnn)
 export(sits_tiles_to_roi)
 export(sits_timeline)
+export(sits_timeseries_to_csv)
 export(sits_to_csv)
 export(sits_to_xlsx)
 export(sits_train)
diff --git a/R/api_csv.R b/R/api_csv.R
index 48af7ad5..8f5d6a9f 100644
--- a/R/api_csv.R
+++ b/R/api_csv.R
@@ -81,3 +81,22 @@
     )
     return(samples)
 }
+#' @title Get samples metadata as CSV
+#' @name .csv_metadata_from_samples
+#' @author Gilberto Camara
+#' @keywords internal
+#' @noRd
+#' @param data        A sits tibble.
+#' @return            A tibble with metadata
+#'
+.csv_metadata_from_samples <- function(data) {
+    # select the parts of the tibble to be saved
+    csv_columns <- .conf("df_sample_columns")
+    csv <- dplyr::select(data, dplyr::all_of(csv_columns))
+    # create a column with the id
+    n_rows_csv <- nrow(csv)
+    id <- tibble::tibble(id = 1:n_rows_csv)
+    # join the two tibbles
+    csv <- dplyr::bind_cols(id, csv)
+    return(csv)
+}
diff --git a/R/sits_classify.R b/R/sits_classify.R
index f81094c4..b5787ca7 100644
--- a/R/sits_classify.R
+++ b/R/sits_classify.R
@@ -299,14 +299,18 @@ sits_classify.raster_cube <- function(data,
                              msg = .conf("messages", ".check_gpu_memory")
         )
         # Calculate available memory from GPU
-        memsize <- floor(gpu_memory - .torch_mem_info())
-        .check_int_parameter(memsize, min = 1,
+        gpu_available_memory <- floor(gpu_memory - .torch_mem_info())
+        .check_int_parameter(gpu_available_memory, min = 1,
                              msg = .conf("messages", ".check_gpu_memory_size")
         )
         proc_bloat <- .conf("processing_bloat_gpu")
     }
     # avoid memory race in Apple MPS
     if (.torch_mps_enabled(ml_model)) {
+        .check_int_parameter(gpu_memory, min = 1, max = 16384,
+                             msg = .conf("messages", ".check_gpu_memory")
+        )
+
         warning(.conf("messages", "sits_classify_mps"),
                 call. = FALSE
         )
diff --git a/R/sits_csv.R b/R/sits_csv.R
index c8a73008..d0e2ae1a 100644
--- a/R/sits_csv.R
+++ b/R/sits_csv.R
@@ -39,14 +39,8 @@ sits_to_csv.sits <- function(data, file = NULL) {
             extensions = "csv",
             file_exists = FALSE
         )
-    # select the parts of the tibble to be saved
-    csv_columns <- .conf("df_sample_columns")
-    csv <- dplyr::select(data, dplyr::all_of(csv_columns))
-    # create a column with the id
-    n_rows_csv <- nrow(csv)
-    id <- tibble::tibble(id = 1:n_rows_csv)
-    # join the two tibbles
-    csv <- dplyr::bind_cols(id, csv)
+    # get metadata
+    csv <- .csv_metadata_from_samples(data)
     # write the CSV file
     if (.has(file))
         utils::write.csv(csv, file, row.names = FALSE, quote = FALSE)
@@ -68,3 +62,43 @@ sits_to_csv.tbl_df <- function(data, file) {
 sits_to_csv.default <- function(data, file) {
     stop(.conf("messages", "sits_to_csv_default"))
 }
+
+#' @title Export a a full sits tibble to the CSV format
+#'
+#' @name sits_timeseries_to_csv
+#'
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#'
+#' @description Converts metadata and data from a sits tibble to a CSV file.
+#'              The CSV file will not contain the actual time
+#'              series. Its columns will be the same as those of a
+#'              CSV file used to retrieve data from
+#'              ground information ("latitude", "longitude", "start_date",
+#'              "end_date", "cube", "label"), plus the all the time series for
+#'              each data
+#' @param  data       Time series (tibble of class "sits").
+#' @param  file       Full path of the exported CSV file
+#'                    (valid file name with extension ".csv").
+#' @return            Return data.frame with CSV columns (optional)
+#'
+#' @examples
+#' csv_file <- paste0(tempdir(), "/cerrado_2classes_ts.csv")
+#' sits_timeseries_to_csv(cerrado_2classes, file = csv_file)
+#' @export
+#'
+sits_timeseries_to_csv <- function(data, file = NULL) {
+    # check the samples are valid
+    data <- .check_samples(data)
+    csv_1 <- .csv_metadata_from_samples(data)
+    csv_2 <- .predictors(data)[-2:0]
+    csv_combined <- dplyr::bind_cols(csv_1, csv_2)
+
+    # write the CSV file
+    if (.has(file))
+        utils::write.csv(csv_combined,
+                         file,
+                         row.names = FALSE,
+                         quote = FALSE)
+
+    return(csv_combined)
+}
diff --git a/man/sits_timeseries_to_csv.Rd b/man/sits_timeseries_to_csv.Rd
new file mode 100644
index 00000000..6854f79d
--- /dev/null
+++ b/man/sits_timeseries_to_csv.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sits_csv.R
+\name{sits_timeseries_to_csv}
+\alias{sits_timeseries_to_csv}
+\title{Export a a full sits tibble to the CSV format}
+\usage{
+sits_timeseries_to_csv(data, file = NULL)
+}
+\arguments{
+\item{data}{Time series (tibble of class "sits").}
+
+\item{file}{Full path of the exported CSV file
+(valid file name with extension ".csv").}
+}
+\value{
+Return data.frame with CSV columns (optional)
+}
+\description{
+Converts metadata and data from a sits tibble to a CSV file.
+             The CSV file will not contain the actual time
+             series. Its columns will be the same as those of a
+             CSV file used to retrieve data from
+             ground information ("latitude", "longitude", "start_date",
+             "end_date", "cube", "label"), plus the all the time series for
+             each data
+}
+\examples{
+csv_file <- paste0(tempdir(), "/cerrado_2classes_ts.csv")
+sits_timeseries_to_csv(cerrado_2classes, file = csv_file)
+}
+\author{
+Gilberto Camara, \email{gilberto.camara@inpe.br}
+}

From 1747bdfcf76bf20a6680339afdb21479d0f12283 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Mon, 16 Dec 2024 17:18:18 +0000
Subject: [PATCH 07/10] add support to multi tiles in summary variance cube

---
 R/sits_summary.R             | 81 +++++++++++++++++++-----------------
 man/summary.variance_cube.Rd |  7 ++--
 2 files changed, 47 insertions(+), 41 deletions(-)

diff --git a/R/sits_summary.R b/R/sits_summary.R
index bf4453b8..9b91ee2a 100644
--- a/R/sits_summary.R
+++ b/R/sits_summary.R
@@ -267,11 +267,12 @@ summary.derived_cube <- function(object, ..., tile = NULL) {
 #' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
 #' @description This is a generic function. Parameters depend on the specific
 #' type of input.
-#' @param  object    Object of class "class_cube"
-#' @param ...        Further specifications for \link{summary}.
-#' @param  tile        Tile to be summarized
-#' @param  intervals Intervals to calculate the quantiles
-#' @param  quantiles Quantiles to be shown
+#' @param  object       Object of class "class_cube"
+#' @param  ...          Further specifications for \link{summary}.
+#' @param  sample_size  The size of samples will be extracted from the variance
+#'                      cube.
+#' @param  intervals    Intervals to calculate the quantiles
+#' @param  quantiles    Quantiles to be shown
 #'
 #' @return A summary of a variance cube
 #'
@@ -299,42 +300,46 @@ summary.derived_cube <- function(object, ..., tile = NULL) {
 #' @export
 summary.variance_cube <- function(
         object, ...,
-        tile = NULL,
         intervals = 0.05,
-        quantiles = c ("75%", "80%", "85%", "90%", "95%", "100%")) {
+        sample_size = 10000,
+        quantiles = c("75%", "80%", "85%", "90%", "95%", "100%")) {
     .check_set_caller("summary_variance_cube")
-    # Pre-conditional check
-    .check_chr_parameter(tile, allow_null = TRUE)
-    # Extract the chosen tile
-    if (!is.null(tile)) {
-        object <- .summary_check_tile(object, tile)
-    }
-    # get sample size
-    sample_size <- .conf("summary_sample_size")
-    # Get tile name
-    tile <- .default(tile, .cube_tiles(object)[[1]])
-    tile <- .cube_filter_tiles(object, tile)
-    # get the bands
-    band <- .tile_bands(tile)
-    # extract the file paths
-    files <- .tile_paths(tile)
-    # read the files with terra
-    r <- .raster_open_rast(files)
-    # get the a sample of the values
-    values <- r |>
-        .raster_sample(size = sample_size, na.rm = TRUE)
-    # scale the values
-    band_conf <- .tile_band_conf(tile, band)
-    scale <- .scale(band_conf)
-    offset <- .offset(band_conf)
-    values <- values * scale + offset
-    # calculate the quantiles
-    mat <- apply(values, 2, function(x){
-        stats::quantile(x, probs = seq(0, 1, intervals))
+    # Get cube labels
+    labels <- .cube_labels(object)
+    # Extract variance values for each tiles using a sample size
+    var_values <- slider::slide(data, function(tile) {
+        # get the bands
+        band <- .tile_bands(tile)
+        # extract the file path
+        file <- .tile_paths(tile)
+        # read the files with terra
+        r <- .raster_open_rast(file)
+        # get the a sample of the values
+        values <- r |>
+            .raster_sample(size = sample_size, na.rm = TRUE)
+        # scale the values
+        band_conf <- .tile_band_conf(tile, band)
+        scale <- .scale(band_conf)
+        offset <- .offset(band_conf)
+        values <- values * scale + offset
+        values
     })
-    colnames(mat) <- .tile_labels(tile)
-
-    return(mat[quantiles, ])
+    # Combine variance values
+    var_values <- dplyr::bind_rows(var_values)
+    # Update columns name
+    colnames(var_values) <- labels
+    # Extract quantile for each column
+    var_values <- dplyr::reframe(
+        var_values,
+        dplyr::across(.cols = dplyr::all_of(labels), function(x) {
+            stats::quantile(x, probs = seq(0, 1, intervals))
+        })
+    )
+    # Update row names
+    percent_intervals <- paste0(seq(from = 0, to = 1, by = intervals)*100, "%")
+    rownames(var_values) <- percent_intervals
+    # Return variance values filtered by quantiles
+    return(var_values[quantiles, ])
 }
 #'
 #'
diff --git a/man/summary.variance_cube.Rd b/man/summary.variance_cube.Rd
index c0ca3405..c713bf95 100644
--- a/man/summary.variance_cube.Rd
+++ b/man/summary.variance_cube.Rd
@@ -7,8 +7,8 @@
 \method{summary}{variance_cube}(
   object,
   ...,
-  tile = NULL,
   intervals = 0.05,
+  sample_size = 10000,
   quantiles = c("75\%", "80\%", "85\%", "90\%", "95\%", "100\%")
 )
 }
@@ -17,10 +17,11 @@
 
 \item{...}{Further specifications for \link{summary}.}
 
-\item{tile}{Tile to be summarized}
-
 \item{intervals}{Intervals to calculate the quantiles}
 
+\item{sample_size}{The size of samples will be extracted from the variance
+cube.}
+
 \item{quantiles}{Quantiles to be shown}
 }
 \value{

From 311291006c18b4c359007e9b7039f17820b6c0a4 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Mon, 16 Dec 2024 17:27:19 +0000
Subject: [PATCH 08/10] update sits_summary code

---
 R/sits_summary.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/sits_summary.R b/R/sits_summary.R
index 9b91ee2a..35d13e20 100644
--- a/R/sits_summary.R
+++ b/R/sits_summary.R
@@ -305,9 +305,9 @@ summary.variance_cube <- function(
         quantiles = c("75%", "80%", "85%", "90%", "95%", "100%")) {
     .check_set_caller("summary_variance_cube")
     # Get cube labels
-    labels <- .cube_labels(object)
+    labels <- unname(.cube_labels(object))
     # Extract variance values for each tiles using a sample size
-    var_values <- slider::slide(data, function(tile) {
+    var_values <- slider::slide(object, function(tile) {
         # get the bands
         band <- .tile_bands(tile)
         # extract the file path

From 8c8bf45454f950058db59da3a4593b6598b429cf Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Mon, 16 Dec 2024 17:35:28 +0000
Subject: [PATCH 09/10] update summary

---
 R/sits_summary.R | 67 ++++++++++++++++++++++--------------------------
 1 file changed, 31 insertions(+), 36 deletions(-)

diff --git a/R/sits_summary.R b/R/sits_summary.R
index 9b91ee2a..184aaea5 100644
--- a/R/sits_summary.R
+++ b/R/sits_summary.R
@@ -195,9 +195,10 @@ summary.raster_cube <- function(object, ..., tile = NULL, date = NULL) {
 #' @title Summary of a derived cube
 #' @author Felipe Souza, \email{felipe.souza@@inpe.br}
 #' @noRd
-#' @param object data cube
+#' @param object       data cube
 #' @param  ...         Further specifications for \link{summary}.
-#' @param tile A \code{tile}.
+#' @param sample_size  The size of samples will be extracted from the variance
+#'                     cube.
 #' @return Summary of a derived cube
 #'
 #' @examples
@@ -225,41 +226,35 @@ summary.raster_cube <- function(object, ..., tile = NULL, date = NULL) {
 #' }
 #'
 #' @export
-summary.derived_cube <- function(object, ..., tile = NULL) {
+summary.derived_cube <- function(object, ..., sample_size = 10000) {
     .check_set_caller("summary_derived_cube")
-    # Pre-conditional check
-    .check_chr_parameter(tile, allow_null = TRUE)
-    # Extract the chosen tile
-    if (!is.null(tile)) {
-        object <- .summary_check_tile(object, tile)
-    }
-    # get sample size
-    sample_size <- .conf("summary_sample_size")
-    # Get tile name
-    tile <- .default(tile, .cube_tiles(object)[[1]])
-    tile <- .cube_filter_tiles(object, tile)
-    # get the bands
-    band <- .tile_bands(tile)
-    .check_num(
-        x = length(band),
-        min = 1,
-        max = 1,
-        is_integer = TRUE
-    )
-    # extract the file paths
-    files <- .tile_paths(tile)
-    # read the files with terra
-    r <- .raster_open_rast(files)
-    # get the a sample of the values
-    values <- r |>
-        .raster_sample(size = sample_size, na.rm = TRUE)
-    # scale the values
-    band_conf <- .tile_band_conf(tile, band)
-    scale <- .scale(band_conf)
-    offset <- .offset(band_conf)
-    sum <- summary(values * scale + offset)
-    colnames(sum) <- .tile_labels(tile)
-    return(sum)
+    # Get cube labels
+    labels <- unname(.cube_labels(object))
+    # Extract variance values for each tiles using a sample size
+    var_values <- slider::slide(object, function(tile) {
+        # get the bands
+        band <- .tile_bands(tile)
+        # extract the file path
+        file <- .tile_paths(tile)
+        # read the files with terra
+        r <- .raster_open_rast(file)
+        # get the a sample of the values
+        values <- r |>
+            .raster_sample(size = sample_size, na.rm = TRUE)
+        # scale the values
+        band_conf <- .tile_band_conf(tile, band)
+        scale <- .scale(band_conf)
+        offset <- .offset(band_conf)
+        values <- values * scale + offset
+        values
+    })
+    # Combine variance values
+    var_values <- dplyr::bind_rows(var_values)
+    var_values <- summary(var_values)
+    # Update columns name
+    colnames(var_values) <- labels
+    # Return summary values
+    return(var_values)
 }
 #' @title  Summarise variance cubes
 #' @method summary variance_cube

From 803aed0025aa3bfead270f2abbaeae50d1c09905 Mon Sep 17 00:00:00 2001
From: Felipe <lipecaso@gmail.com>
Date: Mon, 16 Dec 2024 20:47:01 +0000
Subject: [PATCH 10/10] update summary of class_cube

---
 R/sits_summary.R          | 89 ++++++++++++++++++++-------------------
 man/summary.class_cube.Rd |  4 +-
 2 files changed, 46 insertions(+), 47 deletions(-)

diff --git a/R/sits_summary.R b/R/sits_summary.R
index 15f2efaf..15ad0f91 100644
--- a/R/sits_summary.R
+++ b/R/sits_summary.R
@@ -336,8 +336,6 @@ summary.variance_cube <- function(
     # Return variance values filtered by quantiles
     return(var_values[quantiles, ])
 }
-#'
-#'
 #' @title  Summarize data cubes
 #' @method summary class_cube
 #' @name summary.class_cube
@@ -346,7 +344,6 @@ summary.variance_cube <- function(
 #' type of input.
 #' @param  object    Object of class "class_cube"
 #' @param ...        Further specifications for \link{summary}.
-#' @param  tile        Tile to be summarized
 #'
 #' @return A summary of a classified cube
 #'
@@ -373,46 +370,50 @@ summary.variance_cube <- function(
 #'     summary(label_cube)
 #' }
 #' @export
-#'
-summary.class_cube <- function(object, ..., tile = NULL) {
+summary.class_cube <- function(object, ...) {
     .check_set_caller("summary_class_cube")
-    # Pre-conditional check
-    .check_chr_parameter(tile, allow_null = TRUE)
-    # Extract the chosen tile
-    if (!is.null(tile)) {
-        object <- .summary_check_tile(object, tile)
-    }
-    # Get tile name
-    tile <- .default(tile, .cube_tiles(object)[[1]])
-    tile <- .cube_filter_tiles(object, tile)
-    # get the bands
-    bands <- .tile_bands(tile)
-    .check_chr_parameter(bands, len_min = 1, len_max = 1)
-    # extract the file paths
-    files <- .tile_paths(tile)
-    # read raster files
-    r <- .raster_open_rast(files)
-    # get a frequency of values
-    class_areas <- .raster_freq(r)
-    # transform to km^2
-    cell_size <- .tile_xres(tile) * .tile_yres(tile)
-    class_areas[["area"]] <-  (class_areas[["count"]] * cell_size) / 10^6
-    # change value to character
-    class_areas <- dplyr::mutate(class_areas,
-        value = as.character(.data[["value"]])
-    )
-    # create a data.frame with the labels
-    labels <- .tile_labels(tile)
-    df1 <- tibble::tibble(value = names(labels), class = unname(labels))
-    # join the labels with the areas
-    sum <- dplyr::full_join(df1, class_areas, by = "value")
-    sum <- dplyr::mutate(sum,
-        area_km2 = signif(.data[["area"]], 2),
-        .keep = "unused"
-    )
-    # remove layer information
-    sum_clean <- sum[, -3] |>
-        tidyr::replace_na(list(layer = 1, count = 0, area_km2 = 0))
-    # show the result
-    return(sum_clean)
+    # Get cube labels
+    labels <- unname(.cube_labels(object))
+    # Extract classes values for each tiles using a sample size
+    classes_areas <- slider::slide(object, function(tile) {
+        # get the bands
+        band <- .tile_bands(tile)
+        # extract the file path
+        file <- .tile_paths(tile)
+        # read the files with terra
+        r <- .raster_open_rast(file)
+        # get a frequency of values
+        class_areas <- .raster_freq(r)
+        # transform to km^2
+        cell_size <- .tile_xres(tile) * .tile_yres(tile)
+        class_areas[["area"]] <-  (class_areas[["count"]] * cell_size) / 10^6
+        # change value to character
+        class_areas <- dplyr::mutate(
+            class_areas, value = as.character(.data[["value"]])
+        )
+        # create a data.frame with the labels
+        labels <- .tile_labels(tile)
+        df1 <- tibble::tibble(value = names(labels), class = unname(labels))
+        # join the labels with the areas
+        sum <- dplyr::full_join(df1, class_areas, by = "value")
+        sum <- dplyr::mutate(sum,
+                             area_km2 = signif(.data[["area"]], 2),
+                             .keep = "unused"
+        )
+        # remove layer information
+        sum_clean <- sum[, -3] |>
+            tidyr::replace_na(list(layer = 1, count = 0, area_km2 = 0))
+
+        sum_clean
+    })
+    # Combine tiles areas
+    classes_areas <- dplyr::bind_rows(classes_areas) |>
+        dplyr::group_by(.data[["value"]], .data[["class"]]) |>
+        dplyr::summarise(
+            count = sum(.data[["count"]]),
+            area_km2 = sum(.data[["area_km2"]]),
+            .groups = "keep") |>
+        dplyr::ungroup()
+    # Return classes areas
+    return(classes_areas)
 }
diff --git a/man/summary.class_cube.Rd b/man/summary.class_cube.Rd
index bdaf6a9d..21f93251 100644
--- a/man/summary.class_cube.Rd
+++ b/man/summary.class_cube.Rd
@@ -4,14 +4,12 @@
 \alias{summary.class_cube}
 \title{Summarize data cubes}
 \usage{
-\method{summary}{class_cube}(object, ..., tile = NULL)
+\method{summary}{class_cube}(object, ...)
 }
 \arguments{
 \item{object}{Object of class "class_cube"}
 
 \item{...}{Further specifications for \link{summary}.}
-
-\item{tile}{Tile to be summarized}
 }
 \value{
 A summary of a classified cube