diff --git a/docs/background.md b/docs/background.md
index 907d291..6d76cf0 100644
--- a/docs/background.md
+++ b/docs/background.md
@@ -8,11 +8,11 @@ The **GrIML** processing package is for classifying water bodies from satellite
The **GrIML** post-processing chain follows a linear workflow. Initial rasterised binary classifications denoting water bodies can be inputted to **convert**, **filter** and **merge** into a cohesive ice marginal lake vector dataset, populated with useful **metadata** and analysed with relevant **statistical information**.
-
+[!GrIML summary workflow](https://github.com/GEUS-Glaciology-and-Climate/GrIML/blob/main/other/reporting/figures/griml_workflow_without_gee.png?raw=true)
Each of these post-processing steps is contained within GrIML's modules, and called in turn to perform the entire processing chain. The `griml()` function invokes all post-processing steps.
-
+[!GrIML package structure](https://github.com/GEUS-Glaciology-and-Climate/GrIML/blob/main/other/reporting/figures/griml_package_structure.png?raw=true)
## Project motivation
diff --git a/docs/tutorials-data.md b/docs/tutorials-data.md
index 54c67d6..5531bbf 100644
--- a/docs/tutorials-data.md
+++ b/docs/tutorials-data.md
@@ -84,8 +84,7 @@ iml = gpd.read_file("dataverse_files/20230101-ESA-GRIML-IML-fv1.shp")
iml.plot(color="red")
```
-
-
+
```{important}
Make sure that the file path is correct in order to load the dataset correctly
@@ -99,8 +98,8 @@ iml_d["centroid"] = iml_d.geometry.centroid
iml_d["centroid"].plot(markersize=0.5)
```
-
-
+
+
## Generating statistics
We can extract basic statistics from an ice marginal lake inventory in the dataset series using simple querying. Let's take the 2022 inventory in this example and first determine the number of classified lakes, and then the number of unique lakes:
@@ -236,4 +235,6 @@ ax.set_ylabel('Lake abundance', fontsize=14)
# Show plot
plt.show()
```
+https://github.com/GEUS-Glaciology-and-Climate/GrIML/blob/main/docs/figures/iml_time_series_plot.png?raw=true
+