devAskNewPage(ask = FALSE)
# load the sits library
library(sits)
if (!requireNamespace("sitsdata", quietly = TRUE)) {
stop("Please install package sitsdata\n",
"Please call devtools::install_github('e-sensing/sitsdata')",
call. = FALSE
)
}
# load the sitsdata library
library(sitsdata)
# load a dataset of time series samples for the Mato Grosso region
data("samples_matogrosso_mod13q1")
# create a list to store the results
results <- list()
# Deep Learning - MLP
print("== Accuracy Assessment = DL =======================")
acc_ltae <- sits_kfold_validate(
samples_matogrosso_mod13q1,
folds = 5,
ml_method = sits_lighttae()
)
acc_ltae[["name"]] <- "LightTAE"
results[[length(results) + 1]] <- acc_ltae
# Deep Learning - TempCNN
print("== Accuracy Assessment = TempCNN =======================")
acc_tc <- sits_kfold_validate(
samples_matogrosso_mod13q1,
folds = 5,
ml_method = sits_tempcnn()
)
acc_tc[["name"]] <- "TempCNN"
results[[length(results) + 1]] <- acc_tc
sits_to_xlsx(results, file = file.path(tempdir(), "/accuracy_mato_grosso_dl.xlsx"))
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