diff --git a/.Rbuildignore b/.Rbuildignore
index 314e13a..6cf7893 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -26,3 +26,6 @@ examples_x64
 ^\.github$
 ^doc$
 ^Meta$
+
+vignettes.awk
+_pkgdown.yml
\ No newline at end of file
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
new file mode 100644
index 0000000..ed7650c
--- /dev/null
+++ b/.github/workflows/pkgdown.yaml
@@ -0,0 +1,48 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.4.1
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs
diff --git a/DESCRIPTION b/DESCRIPTION
index 76e0c6b..f4ba85e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: dtwSat
 Type: Package
 Title: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis
 Version: 1.0.0
-Date: 2023-09-20
+Date: 2023-09-21
 Authors@R:
     c(person(given = "Victor",
              family = "Maus",
diff --git a/README.md b/README.md
index a4f1117..031f02c 100644
--- a/README.md
+++ b/README.md
@@ -12,31 +12,37 @@
 
 <!-- badges: end -->
   
+Provides a robust approach to land use mapping using multi-dimensional 
+(multi-band) satellite image time series. By leveraging the Time-Weighted Dynamic 
+Time Warping (TWDTW) distance metric in tandem with a 1 Nearest-Neighbor (1-NN) Classifier,
+this package offers functions to produce land use maps based on distinct seasonality patterns, 
+commonly observed in the phenological cycles of vegetation. The approach is described in 
+Maus et al. (2016) and Maus et al. (2019).
+A primary advantage of TWDTW is its capability to handle irregularly sampled and noisy time series, 
+while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, 
+visualizing temporal patterns, producing land use maps, and visualizing the results.
 
-### Time-Weighted Dynamic Time Warping for satellite image time series analysis
+## Getting Started
 
-The package *dtwSat* provides an implementation of the Time-Weighted
-Dynamic Time Warping (TWDTW) method for land cover mapping using
-multi-band satellite image time series (Maus et al. 2016, 2019).
-*dtwSat* provides full cycle of land cover classification using image
-time series, ranging from selecting temporal patterns to visualizing,
-and assessing the results.
-
-## Installing
-
-Install either from CRAN
+You can install dtwSat from CRAN using the following command:
 
 ``` r
 install.packages("dtwSat")
 ```
 
-or install the development versions from GitHub
+Alternatively, you can install the development version from GitHub:
 
 ``` r
-library(devtools)
 devtools::install_github("vwmaus/dtwSat")
 ```
 
+After installation, you can read the vignette for a quick start guide:
+
+``` r
+vignette("landuse-mapping", "dtwSat")
+```
+
+
 ## References
 
 <div id="refs" class="references csl-bib-body hanging-indent">
diff --git a/vignettes.awk b/vignettes.awk
new file mode 100644
index 0000000..0517265
--- /dev/null
+++ b/vignettes.awk
@@ -0,0 +1,13 @@
+{
+	if (NR == 4) {
+		print("output: rmarkdown::html_vignette")
+	} else if (NR > 4 && NR <= 10) {
+	} else if (NR == 16) {
+		print("\n**For a better version of the dtwSat vignettes see** https://vwmaus.github.io/dtwSat/articles/\n")
+	} else if (NR == 19) {
+		print
+		print("knitr::opts_chunk$set(fig.height = 4.5)")
+		print("knitr::opts_chunk$set(fig.width = 6)")
+	} else
+		print
+}
diff --git a/vignettes/mapping-01-stars.Rmd b/vignettes/landuse-mapping.Rmd
similarity index 92%
rename from vignettes/mapping-01-stars.Rmd
rename to vignettes/landuse-mapping.Rmd
index 1085c05..1954cdd 100644
--- a/vignettes/mapping-01-stars.Rmd
+++ b/vignettes/landuse-mapping.Rmd
@@ -15,8 +15,9 @@ vignette: >
 bibliography: ./../inst/REFERENCES.bib
 ---
 
-```{r, echo=FALSE, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE)
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, dev = "png")
 ```
 
 This vignette offers a concise guide for using version 1.0.0 or higher of the `dtwSat` package to generate a land-use map.
@@ -24,7 +25,7 @@ The package utilizes Time-Weighted Dynamic Time Warping (TWDTW) along with a 1-N
 The subsequent sections will walk you through the process of creating a land-use map based on a set of training samples 
 and a multi-band satellite image time series.
 
-# Reading Training Samples
+## Reading Training Samples
 
 First, let's read a set of training samples that come with the `dtwSat` package installation.
 
@@ -35,7 +36,7 @@ samples <- system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
   st_read(quiet = TRUE)
 ```
 
-# Preparing the Satellite Image Time Series
+## Preparing the Satellite Image Time Series
 
 The `dtwSat` package supports satellite images read into R using the `stars` package.
 The instalation comes with a set of MOD13Q1 images for a region within the Brazilian Amazon.
@@ -61,15 +62,14 @@ dc <- read_stars(tif_files,
                  along = list(time = acquisition_date),
                  RasterIO = list(bands = 1:6)) |>
   st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
-  split(c("band")) |>
-  split(c("time"))
+  split(c("band"))
 ```
 
 Note that it's important to set the date for each observation using the parameter `along`.
-This will produce a 4D array (data-cube) that will be collapsed into a 2D array by splitting 
-the 'band' and 'time' dimensions. This prepares the data for training the TWDTW-1NN model.
+This will produce a 4D array (data-cube) that will be collapsed into a 3D array by converting 
+the 'band' dimension into attributes. This prepares the data for training the TWDTW-1NN model.
 
-# Create TWDTW-KNN1 model
+## Create TWDTW-KNN1 model
 
 ```{r , echo = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
 twdtw_model <- twdtw_knn1(x = dc, 
@@ -92,7 +92,7 @@ plot(twdtw_model)
 ```
 
 
-# Land Use Prediction
+## Land Use Prediction
 
 Finally, we predict the land-use classes for each pixel location in the data-cube:
 
@@ -118,7 +118,7 @@ Ultimately, we can write the map to a TIFF file we can use:
 write_stars(lu_map, "lu_map.tif")
 ```
 
-# Further Reading
+## Further Reading
 
 This introduction outlined the use of `dtwSat` for land-use mapping. 
 For more in-depth information, refer to the papers by @Maus:2016 and @Maus:2019 and the 
@@ -127,4 +127,4 @@ For more in-depth information, refer to the papers by @Maus:2016 and @Maus:2019
 For additional details on how to manage input and output satellite images, 
 [`check` the stars documentation](https://cran.r-project.org/web/packages/stars).
 
-# References
+## References