diff --git a/R/api_check.R b/R/api_check.R
index 4c4ba74e3..9cb19c345 100644
--- a/R/api_check.R
+++ b/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))
 }
diff --git a/R/api_classify.R b/R/api_classify.R
index 3eff643a1..e2abdb526 100755
--- a/R/api_classify.R
+++ b/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(
diff --git a/R/api_combine_predictions.R b/R/api_combine_predictions.R
index 3bb915a8e..33d20326f 100644
--- a/R/api_combine_predictions.R
+++ b/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
diff --git a/R/api_label_class.R b/R/api_label_class.R
index 5327df35a..fadb3fa86 100644
--- a/R/api_label_class.R
+++ b/R/api_label_class.R
@@ -148,10 +148,10 @@
 .label_fn_majority <- function() {
     label_fn <- function(values) {
         # Used to check values (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         values <- C_label_max_prob(values)
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return values
         values
     }
diff --git a/R/api_mixture_model.R b/R/api_mixture_model.R
index 91de3d077..aec2630f7 100644
--- a/R/api_mixture_model.R
+++ b/R/api_mixture_model.R
@@ -160,7 +160,7 @@
     em_mtx <- .endmembers_as_matrix(em)
     mixture_fn <- function(values) {
         # Check values length
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Process NNLS solver and return
         values <- C_nnls_solver_batch(
             x = as.matrix(values),
@@ -168,7 +168,7 @@
             rmse = rmse
         )
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return values
         values
     }
diff --git a/R/api_reclassify.R b/R/api_reclassify.R
index bec3ba41b..cbe40ea9f 100644
--- a/R/api_reclassify.R
+++ b/R/api_reclassify.R
@@ -158,7 +158,7 @@
             stop(.conf("messages", ".reclassify_fn_cube_mask"))
         }
         # Used to check values (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Convert to character vector
         values <- as.character(values)
         mask_values <- as.character(mask_values)
@@ -185,12 +185,12 @@
         # Get values as numeric
         values <- matrix(
             data = labels_code[match(values, labels)],
-            nrow = n_input_pixels
+            nrow = input_pixels
         )
         # Mask NA values
         values[is.na(env[["mask"]])] <- NA
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return values
         values
     }
diff --git a/R/api_smooth.R b/R/api_smooth.R
index 3e42d8a6a..d30d924f4 100644
--- a/R/api_smooth.R
+++ b/R/api_smooth.R
@@ -172,7 +172,7 @@
     # Define smooth function
     smooth_fn <- function(values, block) {
         # Check values length
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Compute logit
         values <- log(values / (rowSums(values) - values))
         # Process Bayesian
@@ -187,7 +187,7 @@
         # Compute inverse logit
         values <- exp(values) / (exp(values) + 1)
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return values
         values
     }
diff --git a/R/api_uncertainty.R b/R/api_uncertainty.R
index 6cecad65a..1dc622b18 100644
--- a/R/api_uncertainty.R
+++ b/R/api_uncertainty.R
@@ -239,12 +239,12 @@
     # Define uncertainty function
     uncert_fn <- function(values) {
         # Used in check (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Process least confidence
         # return a matrix[rows(values),1]
         values <- C_least_probs(values)
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return data
         values
     }
@@ -260,11 +260,11 @@
     # Define uncertainty function
     uncert_fn <- function(values) {
         # Used in check (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Process least confidence
         values <- C_entropy_probs(values) # return a matrix[rows(values),1]
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return data
         values
     }
@@ -280,11 +280,11 @@
     # Define uncertainty function
     uncert_fn <- function(values) {
         # Used in check (below)
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Process margin
         values <- C_margin_probs(values) # return a matrix[rows(data),1]
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return data
         values
     }
diff --git a/R/api_variance.R b/R/api_variance.R
index 63d13f92d..99e390ba3 100644
--- a/R/api_variance.R
+++ b/R/api_variance.R
@@ -176,7 +176,7 @@
     # Define smooth function
     smooth_fn <- function(values, block) {
         # Check values length
-        n_input_pixels <- nrow(values)
+        input_pixels <- nrow(values)
         # Compute logit
         values <- log(values / (rowSums(values) - values))
         # Process variance
@@ -188,7 +188,7 @@
             neigh_fraction = neigh_fraction
         )
         # Are the results consistent with the data input?
-        .check_processed_values(values, n_input_pixels)
+        .check_processed_values(values, input_pixels)
         # Return values
         values
     }
diff --git a/R/sits_lighttae.R b/R/sits_lighttae.R
index e4e521ab3..ce5799a0b 100644
--- a/R/sits_lighttae.R
+++ b/R/sits_lighttae.R
@@ -328,7 +328,7 @@ sits_lighttae <- function(samples = NULL,
             # Unserialize model
             torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Transform input into a 3D tensor
             # Reshape the 2D matrix into a 3D array
             n_samples <- nrow(values)
@@ -362,7 +362,7 @@ sits_lighttae <- function(samples = NULL,
             )
             # Are the results consistent with the data input?
             .check_processed_values(
-                values = values, n_input_pixels = n_input_pixels
+                values = values, input_pixels = input_pixels
             )
             # Update the columns names to labels
             colnames(values) <- labels
diff --git a/R/sits_machine_learning.R b/R/sits_machine_learning.R
index d1f2e6ce2..c8b6dccef 100644
--- a/R/sits_machine_learning.R
+++ b/R/sits_machine_learning.R
@@ -79,13 +79,13 @@ sits_rfor <- function(samples = NULL, num_trees = 100, mtry = NULL, ...) {
             # Verifies if randomForest package is installed
             .check_require_packages("randomForest")
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Do classification
             values <- stats::predict(
                 object = model, newdata = values, type = "prob"
             )
             # Are the results consistent with the data input?
-            .check_processed_values(values, n_input_pixels)
+            .check_processed_values(values, input_pixels)
             # Reorder matrix columns if needed
             if (any(labels != colnames(values))) {
                 values <- values[, labels]
@@ -193,7 +193,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
             # Verifies if e1071 package is installed
             .check_require_packages("e1071")
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Performs data normalization
             values <- .pred_normalize(pred = values, stats = ml_stats)
             # Do classification
@@ -203,7 +203,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
             # Get the predicted probabilities
             values <- attr(values, "probabilities")
             # Are the results consistent with the data input?
-            .check_processed_values(values, n_input_pixels)
+            .check_processed_values(values, input_pixels)
             # Reorder matrix columns if needed
             if (any(labels != colnames(values))) {
                 values <- values[, labels]
@@ -337,14 +337,14 @@ sits_xgboost <- function(samples = NULL, learning_rate = 0.15,
             # Verifies if xgboost package is installed
             .check_require_packages("xgboost")
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Do classification
             values <- stats::predict(
                 object = model, as.matrix(values), ntreelimit = ntreelimit,
                 reshape = TRUE
             )
             # Are the results consistent with the data input?
-            .check_processed_values(values, n_input_pixels)
+            .check_processed_values(values, input_pixels)
             # Update the columns names to labels
             colnames(values) <- labels
             return(values)
diff --git a/R/sits_mlp.R b/R/sits_mlp.R
index 025520490..624e87cec 100644
--- a/R/sits_mlp.R
+++ b/R/sits_mlp.R
@@ -284,7 +284,7 @@ sits_mlp <- function(samples = NULL,
             # Unserialize model
             torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Performs data normalization
             values <- .pred_normalize(pred = values, stats = ml_stats)
             # Transform input into matrix
@@ -312,7 +312,7 @@ sits_mlp <- function(samples = NULL,
             )
             # Are the results consistent with the data input?
             .check_processed_values(
-                values = values, n_input_pixels = n_input_pixels
+                values = values, input_pixels = input_pixels
             )
             # Update the columns names to labels
             colnames(values) <- labels
diff --git a/R/sits_resnet.R b/R/sits_resnet.R
index 7b7ea8215..68f53ecb8 100644
--- a/R/sits_resnet.R
+++ b/R/sits_resnet.R
@@ -376,7 +376,7 @@ sits_resnet <- function(samples = NULL,
             # Unserialize model
             torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Transform input into a 3D tensor
             # Reshape the 2D matrix into a 3D array
             n_samples <- nrow(values)
@@ -409,7 +409,7 @@ sits_resnet <- function(samples = NULL,
                 x = torch::torch_tensor(values, device = "cpu")
             )
             .check_processed_values(
-                values = values, n_input_pixels = n_input_pixels
+                values = values, input_pixels = input_pixels
             )
             # Update the columns names to labels
             colnames(values) <- labels
diff --git a/R/sits_tae.R b/R/sits_tae.R
index 1028c2e99..cccdfb9c2 100644
--- a/R/sits_tae.R
+++ b/R/sits_tae.R
@@ -295,7 +295,7 @@ sits_tae <- function(samples = NULL,
             # Unserialize model
             torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Transform input into a 3D tensor
             # Reshape the 2D matrix into a 3D array
             n_samples <- nrow(values)
@@ -329,7 +329,7 @@ sits_tae <- function(samples = NULL,
             )
             # Are the results consistent with the data input?
             .check_processed_values(
-                values = values, n_input_pixels = n_input_pixels
+                values = values, input_pixels = input_pixels
             )
             # Update the columns names to labels
             colnames(values) <- labels
diff --git a/R/sits_tempcnn.R b/R/sits_tempcnn.R
index 1c16e6b95..f91318182 100644
--- a/R/sits_tempcnn.R
+++ b/R/sits_tempcnn.R
@@ -346,7 +346,7 @@ sits_tempcnn <- function(samples = NULL,
             # Unserialize model
             torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
             # Used to check values (below)
-            n_input_pixels <- nrow(values)
+            input_pixels <- nrow(values)
             # Transform input into a 3D tensor
             # Reshape the 2D matrix into a 3D array
             n_samples <- nrow(values)
@@ -381,7 +381,7 @@ sits_tempcnn <- function(samples = NULL,
             )
             # Are the results consistent with the data input?
             .check_processed_values(
-                values = values, n_input_pixels = n_input_pixels
+                values = values, input_pixels = input_pixels
             )
             # Update the columns names to labels
             colnames(values) <- labels