diff --git a/NAMESPACE b/NAMESPACE
index 77420af6..bfdf7a25 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -129,6 +129,8 @@ S3method(.raster_yres,terra)
 S3method(.reg_s2tile_convert,dem_cube)
 S3method(.reg_s2tile_convert,grd_cube)
 S3method(.reg_s2tile_convert,rtc_cube)
+S3method(.samples_alloc_strata,class_cube)
+S3method(.samples_alloc_strata,class_vector_cube)
 S3method(.slice_dfr,numeric)
 S3method(.source_collection_access_test,"mpc_cube_sentinel-1-grd")
 S3method(.source_collection_access_test,cdse_cube)
@@ -198,6 +200,7 @@ S3method(.source_tile_get_bbox,stac_cube)
 S3method(.tile,default)
 S3method(.tile,raster_cube)
 S3method(.tile_area_freq,class_cube)
+S3method(.tile_area_freq,class_vector_cube)
 S3method(.tile_area_freq,default)
 S3method(.tile_area_freq,raster_cube)
 S3method(.tile_as_sf,default)
diff --git a/R/api_cube.R b/R/api_cube.R
index afa69361..840181ff 100644
--- a/R/api_cube.R
+++ b/R/api_cube.R
@@ -143,23 +143,10 @@ NULL
 #'
 #' @return A \code{vector} with the areas of the cube labels.
 .cube_class_areas <- function(cube) {
-    .check_is_class_cube(cube)
-    labels_cube <- sits_labels(cube)
-
     # Get area for each class for each row of the cube
     freq_lst <- slider::slide(cube, function(tile) {
         # Get the frequency count and value for each labelled image
         freq <- .tile_area_freq(tile)
-        # pixel area
-        # convert the area to hectares
-        # assumption: spatial resolution unit is meters
-        area <- freq[["count"]] * .tile_xres(tile) * .tile_yres(tile) / 10000
-        # Include class names
-        freq <- dplyr::mutate(
-            freq,
-            area = area,
-            class = labels_cube[as.character(freq[["value"]])]
-        )
         return(freq)
     })
     # Get a tibble by binding the row (duplicated labels with different counts)
diff --git a/R/api_samples.R b/R/api_samples.R
index 4bcaa023..69f08358 100644
--- a/R/api_samples.R
+++ b/R/api_samples.R
@@ -203,3 +203,82 @@
         })
     })
 }
+#' @title Allocate points for stratified sampling for accuracy estimation
+#' @name .samples_alloc_strata
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @param cube   Classified data cube (raster or vector)
+#' @param samples_class Matrix with sampling design to be allocated
+#' @param dots     Other params for the function
+#' @param multicores Number of cores to work in parallel
+#' @param progress  Show progress bar?
+#'
+#' @return Points resulting from stratified sampling
+#' @keywords internal
+#' @noRd
+.samples_alloc_strata <- function(cube,
+                                  samples_class, ...,
+                                  multicores = 2,
+                                  progress = TRUE){
+    UseMethod(".samples_alloc_strata", cube)
+}
+#' @export
+.samples_alloc_strata.class_cube <- function(cube,
+                                             samples_class, ...,
+                                             multicores = 2,
+                                             progress = TRUE){
+    # estimate size
+    size <- ceiling(max(samples_class) / nrow(cube))
+    # get labels
+    labels <- names(samples_class)
+    n_labels <- length(labels)
+    # Prepare parallel processing
+    .parallel_start(workers = multicores)
+    on.exit(.parallel_stop(), add = TRUE)
+    # Create assets as jobs
+    cube_assets <- .cube_split_assets(cube)
+    # Process each asset in parallel
+    samples <- .jobs_map_parallel_dfr(cube_assets, function(tile) {
+        robj <- .raster_open_rast(.tile_path(tile))
+        cls <- data.frame(id = 1:n_labels,
+                          cover = labels)
+        levels(robj) <- cls
+        samples_sv <- terra::spatSample(
+            x = robj,
+            size = size,
+            method = "stratified",
+            as.points = TRUE
+        )
+        samples_sf <- sf::st_as_sf(samples_sv)
+        samples_sf <- dplyr::mutate(samples_sf,
+                                    label = labels[.data[["cover"]]])
+        samples_sf <- sf::st_transform(samples_sf, crs = "EPSG:4326")
+    }, progress = progress)
+
+    samples <- purrr::map_dfr(labels, function(lab) {
+        samples_class <- samples |>
+            dplyr::filter(.data[["label"]] == lab) |>
+            dplyr::slice_sample(n = samples_class[lab])
+    })
+
+    return(samples)
+}
+#' @export
+.samples_alloc_strata.class_vector_cube <- function(cube,
+                                             samples_class, ...,
+                                             multicores = 2,
+                                             progress = TRUE){
+
+    segments_cube <- slider::slide_dfr(cube, function(tile){
+        # Open segments and transform them to tibble
+        segments <- .segments_read_vec(tile)
+    })
+    # Retrieve the required number of segments per class
+    samples_lst <- segments_cube |>
+        dplyr::group_by(.data[["class"]]) |>
+        dplyr::group_map(function(cl, class){
+            samples_class <- sf::st_sample(cl, samples_class[class[["class"]]])
+            sf_samples <- sf::st_sf(class, geometry = samples_class)
+        })
+    samples <- dplyr::bind_rows(samples_lst)
+    return(samples)
+}
diff --git a/R/api_tile.R b/R/api_tile.R
index b653f8e4..17c604cd 100644
--- a/R/api_tile.R
+++ b/R/api_tile.R
@@ -1314,6 +1314,33 @@ NULL
     r_obj <- .raster_open_rast(.tile_path(tile))
     # Retrieve the frequency
     freq <- tibble::as_tibble(.raster_freq(r_obj))
+    # get labels
+    labels <- .tile_labels(tile)
+    # pixel area
+    # convert the area to hectares
+    # assumption: spatial resolution unit is meters
+    area <- freq[["count"]] * .tile_xres(tile) * .tile_yres(tile) / 10000
+    # Include class names
+    freq <- dplyr::mutate(
+        freq,
+        area = area,
+        class = labels[as.character(freq[["value"]])]
+    )
+    # Return frequencies
+    freq
+}
+#' @export
+.tile_area_freq.class_vector_cube <- function(tile) {
+    # Open segments
+    segments <- .segments_read_vec(tile)
+    segments[["area"]] <- sf::st_area(segments)
+    segments <- sf::st_drop_geometry(segments)
+    segments <- units::drop_units(segments)
+    # Retrieve the area
+    freq <- segments |>
+        dplyr::group_by(class) |>
+        dplyr::summarise(area = sum(.data[["area"]])) |>
+        dplyr::select(c(dplyr::all_of("area"), dplyr::all_of("class")))
     # Return frequencies
     freq
 }
diff --git a/R/sits_classify.R b/R/sits_classify.R
index 9a2c05fb..e472f3b1 100644
--- a/R/sits_classify.R
+++ b/R/sits_classify.R
@@ -253,6 +253,8 @@ sits_classify.raster_cube <- function(data,
         .check_filter_fn(filter_fn)
     # Retrieve the samples from the model
     samples <- .ml_samples(ml_model)
+    # Retrieve the bands from the model
+    bands <- .ml_bands(ml_model)
     # Do the samples and tile match their timeline length?
     .check_samples_tile_match_timeline(samples = samples, tile = data)
     # Do the samples and tile match their bands?
@@ -263,7 +265,7 @@ sits_classify.raster_cube <- function(data,
     # Check minimum memory needed to process one block
     job_memsize <- .jobs_memsize(
         job_size = .block_size(block = block, overlap = 0),
-        npaths = length(.tile_paths(data)) + length(.ml_labels(ml_model)),
+        npaths = length(.tile_paths(data, bands)) + length(.ml_labels(ml_model)),
         nbytes = 8,
         proc_bloat = proc_bloat
     )
diff --git a/R/sits_plot.R b/R/sits_plot.R
index c8cb8fec..c86f6d12 100644
--- a/R/sits_plot.R
+++ b/R/sits_plot.R
@@ -524,7 +524,7 @@ plot.vector_cube <- function(x, ...,
                              tile = x[["tile"]][[1]],
                              dates = NULL,
                              seg_color = "black",
-                             line_width = 1,
+                             line_width = 0.3,
                              palette = "RdYlGn",
                              rev = FALSE,
                              scale = 1.0,
diff --git a/R/sits_sample_functions.R b/R/sits_sample_functions.R
index 7c499932..57bb00c1 100644
--- a/R/sits_sample_functions.R
+++ b/R/sits_sample_functions.R
@@ -284,15 +284,14 @@ sits_reduce_imbalance <- function(samples,
 #'     sampling_design <- sits_sampling_design(label_cube, expected_ua)
 #' }
 #' @export
-sits_sampling_design <- function(cube,
+sits_sampling_design <- function(cube, ...,
                                  expected_ua = 0.75,
                                  std_err = 0.01,
                                  rare_class_prop = 0.1) {
     .check_set_caller("sits_sampling_design")
     # check the cube is valid
-    .check_raster_cube_files(cube)
-    # check cube is class cube
-    .check_is_class_cube(cube)
+    .check_that(inherits(cube, "class_cube") ||
+                    inherits(cube, "class_vector_cube"))
     # get the labels
     labels <- .cube_labels(cube)
     n_labels <- length(labels)
@@ -304,10 +303,13 @@ sits_sampling_design <- function(cube,
     .check_that(length(expected_ua) == n_labels)
     # check names of labels
     .check_that(all(labels %in% names(expected_ua)))
-    # adjust names to match cube labels
-    expected_ua <- expected_ua[labels]
     # get cube class areas
     class_areas <- .cube_class_areas(cube)
+    # check that names of class areas are contained in the labels
+    .check_that(all(names(class_areas) %in% labels),
+                msg = .conf("messages", "sits_sampling_design_labels"))
+    # adjust names to match cube labels
+    expected_ua <- expected_ua[names(class_areas)]
     # calculate proportion of class areas
     prop <- class_areas / sum(class_areas)
     # standard deviation of the stratum
@@ -315,8 +317,9 @@ sits_sampling_design <- function(cube,
     # calculate sample size
     sample_size <-  round((sum(prop * std_dev) / std_err) ^ 2)
     # determine "Equal" allocation
-    equal <- rep(round(sample_size / n_labels), n_labels)
-    names(equal) <- labels
+    n_classes <- length(class_areas)
+    equal <- rep(round(sample_size / n_classes), n_classes)
+    names(equal) <- names(class_areas)
     # find out the classes which are rare
     rare_classes <- prop[prop <= rare_class_prop]
     #  Determine allocation possibilities
@@ -418,66 +421,44 @@ sits_stratified_sampling <- function(cube,
     .check_set_caller("sits_stratified_sampling")
     # check the cube is valid
     .check_raster_cube_files(cube)
-    # check cube is class cube
-    .check_is_class_cube(cube)
+    # check the cube is valid
+    .check_that(inherits(cube, "class_cube") ||
+                    inherits(cube, "class_vector_cube"))
     # get the labels
     labels <- .cube_labels(cube)
     n_labels <- length(labels)
     # check number of labels
-    .check_that(nrow(sampling_design) == n_labels)
+    .check_that(nrow(sampling_design) <= n_labels)
     # check names of labels
     .check_that(all(rownames(sampling_design) %in% labels))
     # check allocation method
     .check_that(alloc %in% colnames(sampling_design),
-                msg = .conf("messages", "sits_sampling_design_alloc"))
+                msg = .conf("messages", "sits_stratified_sampling_alloc"))
     # retrieve samples class
     samples_class <- unlist(sampling_design[, alloc])
     # check samples class
     .check_int_parameter(samples_class, is_named = TRUE,
-            msg = .conf("messages", "sits_sampling_design_samples")
+            msg = .conf("messages", "sits_stratified_sampling_samples")
     )
     .check_int_parameter(multicores, min = 1, max = 2048)
     .check_progress(progress)
     # name samples class
-    names(samples_class) <- rownames(sampling_design)
+    # names(samples_class) <- rownames(sampling_design)
     # include overhead
     samples_class <- ceiling(samples_class * overhead)
-    # estimate size
-    size <- ceiling(max(samples_class) / nrow(cube))
-    # Prepare parallel processing
-    .parallel_start(workers = multicores)
-    on.exit(.parallel_stop(), add = TRUE)
-    # Create assets as jobs
-    cube_assets <- .cube_split_assets(cube)
-    # Process each asset in parallel
-    samples <- .jobs_map_parallel_dfr(cube_assets, function(tile) {
-        robj <- .raster_open_rast(.tile_path(tile))
-        cls <- data.frame(id = 1:n_labels,
-                          cover = labels)
-        levels(robj) <- cls
-        samples_sv <- terra::spatSample(
-            x = robj,
-            size = size,
-            method = "stratified",
-            as.points = TRUE
-        )
-        samples_sf <- sf::st_as_sf(samples_sv)
-        samples_sf <- dplyr::mutate(samples_sf,
-                                    label = labels[.data[["cover"]]])
-        samples_sf <- sf::st_transform(samples_sf, crs = "EPSG:4326")
-    }, progress = progress)
 
-    samples <- purrr::map_dfr(labels, function(lab) {
-        samples_class <- samples |>
-            dplyr::filter(.data[["label"]] == lab) |>
-            dplyr::slice_sample(n = samples_class[lab])
-    })
+    # call function to allocate sample per strata
+    samples <- .samples_alloc_strata(
+        cube = cube,
+        samples_class = samples_class,
+        multicores = multicores)
+
     if (.has(shp_file)) {
         .check_that(tools::file_ext(shp_file) == "shp",
-                    msg = .conf("messages", "sits_sampling_design_shp")
+                    msg = .conf("messages", "sits_stratified_sampling_shp")
         )
         sf::st_write(samples, shp_file, append = FALSE)
-        message(.conf("messages", "sits_sampling_design_shp_save"), shp_file)
+        message(.conf("messages", "sits_stratified_sampling_shp_save"), shp_file)
     }
     return(samples)
 }
diff --git a/inst/extdata/config_messages.yml b/inst/extdata/config_messages.yml
index 3b64cc5b..c7c1c360 100644
--- a/inst/extdata/config_messages.yml
+++ b/inst/extdata/config_messages.yml
@@ -410,11 +410,8 @@ sits_reduce_imbalance_samples: "number of samples to undersample for large class
 sits_resnet: "wrong input parameters - see example in documentation"
 sits_rfor: "wrong input parameters - see example in documentation"
 sits_sample: "invalid frac parameter - values should be btw 0.0 and 2.0"
-sits_sampling_design: "expected values of user's accuracy should contain names of labels"
-sits_sampling_design_alloc: "allocation method is not included in sampling design"
-sits_sampling_design_samples: "number of samples per allocation method should be integer"
-sits_sampling_design_shp: "invalid shapefile name"
-sits_sampling_design_shp_save: "saved allocation in shapefile"
+sits_sampling_design: "sampling design only runs in classified cubes"
+sits_sampling_design_labels: "names of classes in cube do not match labels in expected_ua"
 sits_select: "input should be a valid set of training samples or a non-classified data cube"
 sits_segment: "wrong input parameters - see example in documentation"
 sits_slic: "wrong input parameters - see example in documentation"
@@ -426,6 +423,10 @@ sits_som_evaluate_cluster: "wrong input data; please run sits_som_map first"
 sits_som_map: "number of samples should be greater than number of neurons"
 sits_som_map_grid_size: "recommended values for grid_xdim and grid_ydim are "
 sits_stratified_sampling: "labels in sampling design do not match labels in cube"
+sits_stratified_sampling_alloc: "allocation method is not included in sampling design"
+sits_stratified_sampling_samples: "number of samples per allocation method should be integer"
+sits_stratified_sampling_shp: "invalid shapefile name"
+sits_stratified_sampling_shp_save: "saved allocation in shapefile"
 sits_svm: "wrong input parameters - see example in documentation"
 sits_tae: "wrong input parameters - see example in documentation"
 sits_tempcnn: "wrong input parameters - see example in documentation"
diff --git a/man/plot.vector_cube.Rd b/man/plot.vector_cube.Rd
index c57d1e82..b4dd3e3c 100644
--- a/man/plot.vector_cube.Rd
+++ b/man/plot.vector_cube.Rd
@@ -14,7 +14,7 @@
   tile = x[["tile"]][[1]],
   dates = NULL,
   seg_color = "black",
-  line_width = 1,
+  line_width = 0.3,
   palette = "RdYlGn",
   rev = FALSE,
   scale = 1,
diff --git a/man/sits_sampling_design.Rd b/man/sits_sampling_design.Rd
index f04fc4c7..ad56310b 100644
--- a/man/sits_sampling_design.Rd
+++ b/man/sits_sampling_design.Rd
@@ -6,6 +6,7 @@
 \usage{
 sits_sampling_design(
   cube,
+  ...,
   expected_ua = 0.75,
   std_err = 0.01,
   rare_class_prop = 0.1